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The issue here is the finality of a probability of zero. Out of the three 15-letter words, it turns out that "nongovernmental" is in the dictionary, but if it hadn't been, if somehow our corpus of words had missed it, then the probability of that whole phrase would have been zero. It seems that is too strict; there must be some "real" words that are not in our dictionary, so we shouldn't give them probability zero. There is also a question of likelyhood of being a "real" word. It does seem that "neverbeforeseen" is more English-like than "zqbhjhsyefvvjqc", and so perhaps should have a higher probability.We can address this by assigning a non-zero probability to words that are not in the dictionary. This is even more important when it comes to multi-word phrases (such as bigrams), because it is more likely that a legitimate one will appear that has not been observed before.We can think of our model as being overly spiky; it has a spike of probability mass wherever a word or phrase occurs in the corpus. What we would like to do is *smooth* over those spikes so that we get a model that does not depend on the details of our corpus. The process of "fixing" the model is called *smoothing*.For example, Laplace was asked what's the probability of the sun rising tomorrow. From data that it has risen $n/n$ times for the last *n* days, the maximum liklihood estimator is 1. But Laplace wanted to balance the data with the possibility that tomorrow, either it will rise or it won't, so he came up with $(n + 1) / (n + 2)$. What we know is little, and what we are ignorant of is immense.— Pierre Simon Laplace, 1749-1827 | def pdist_additive_smoothed(counter, c=1):
"""The probability of word, given evidence from the counter.
Add c to the count for each item, plus the 'unknown' item."""
N = sum(counter.values()) # Amount of evidence
Nplus = N + c * (len(counter) + 1) # Evidence plus fake observations
return lambda word: (counter[word] + c) / Nplus
P1w = pdist_additive_smoothed(COUNTS1)
P1w('neverbeforeseen') | _____no_output_____ | MIT | ipynb/How to Do Things with Words.ipynb | mikiec84/pytudes |
But now there's a problem ... we now have previously-unseen words with non-zero probabilities. And maybe 10-12 is about right for words that are observed in text: that is, if I'm *reading* a new text, the probability that the next word is unknown might be around 10-12. But if I'm *manufacturing* 20-letter sequences at random, the probability that one will be a word is much, much lower than 10-12. Look what happens: | segment.cache.clear()
segment('thisisatestofsegmentationofalongsequenceofwords') | _____no_output_____ | MIT | ipynb/How to Do Things with Words.ipynb | mikiec84/pytudes |
There are two problems: First, we don't have a clear model of the unknown words. We just say "unknown" butwe don't distinguish likely unknown from unlikely unknown. For example, is a 8-character unknown more likely than a 20-character unknown?Second, we don't take into account evidence from *parts* of the unknown. For example, "unglobulate" versus "zxfkogultae".For our next approach, *Good - Turing* smoothing re-estimates the probability of zero-count words, based on the probability of one-count words (and can also re-estimate for higher-number counts, but that is less interesting).I. J. Good (1916 - 2009) Alan Turing (1812 - 1954)So, how many one-count words are there in `COUNTS`? (There aren't any in `COUNTS1`.) And what are the word lengths of them? Let's find out: | singletons = (w for w in COUNTS if COUNTS[w] == 1)
lengths = map(len, singletons)
Counter(lengths).most_common()
1357 / sum(COUNTS.values())
hist(lengths, bins=len(set(lengths)));
def pdist_good_turing_hack(counter, onecounter, base=1/26., prior=1e-8):
"""The probability of word, given evidence from the counter.
For unknown words, look at the one-counts from onecounter, based on length.
This gets ideas from Good-Turing, but doesn't implement all of it.
prior is an additional factor to make unknowns less likely.
base is how much we attenuate probability for each letter beyond longest."""
N = sum(counter.values())
N2 = sum(onecounter.values())
lengths = map(len, [w for w in onecounter if onecounter[w] == 1])
ones = Counter(lengths)
longest = max(ones)
return (lambda word:
counter[word] / N if (word in counter)
else prior * (ones[len(word)] / N2 or
ones[longest] / N2 * base ** (len(word)-longest)))
# Redefine P1w
P1w = pdist_good_turing_hack(COUNTS1, COUNTS)
segment.cache.clear()
segment('thisisatestofsegmentationofaverylongsequenceofwords') | _____no_output_____ | MIT | ipynb/How to Do Things with Words.ipynb | mikiec84/pytudes |
That was somewhat unsatisfactory. We really had to crank up the prior, specifically because the process of running `segment` generates so many non-word candidates (and also because there will be fewer unknowns with respect to the billion-word `WORDS1` than with respect to the million-word `WORDS`). It would be better to separate out the prior from the word distribution, so that the same distribution could be used for multiple tasks, not just for this one.Now let's think for a short while about smoothing **bigram** counts. Specifically, what if we haven't seen a bigram sequence, but we've seen both words individually? For example, to evaluate P("Greenland") in the phrase "turn left at Greenland", we might have three pieces of evidence: P("Greenland") P("Greenland" | "at") P("Greenland" | "left", "at") Presumably, the first would have a relatively large count, and thus large reliability, while the second and third would have decreasing counts and reliability. With *interpolation smoothing* we combine all three pieces of evidence, with a linear combination: $P(w_3 \mid w_1w_2) = c_1 P(w_3) + c_2 P(w_3 \mid w_2) + c_3 P(w_3 \mid w_1w_2)$How do we choose $c_1, c_2, c_3$? By experiment: train on training data, maximize $c$ values on development data, then evaluate on test data. However, when we do this, we are saying, with probability $c_1$, that a word can appear anywhere, regardless of previous words. But some words are more free to do that than other words. Consider two words with similar probability: | print P1w('francisco')
print P1w('individuals') | 7.73314623661e-05
7.72494966889e-05
| MIT | ipynb/How to Do Things with Words.ipynb | mikiec84/pytudes |
They have similar unigram probabilities but differ in their freedom to be the second word of a bigram: | print [bigram for bigram in COUNTS2 if bigram.endswith('francisco')]
print [bigram for bigram in COUNTS2 if bigram.endswith('individuals')] | ['are individuals', 'other individuals', 'on individuals', 'infected individuals', 'in individuals', 'where individuals', 'or individuals', 'which individuals', 'to individuals', 'both individuals', 'help individuals', 'more individuals', 'interested individuals', 'from individuals', '<S> individuals', 'income individuals', 'these individuals', 'about individuals', 'the individuals', 'among individuals', 'some individuals', 'those individuals', 'by individuals', 'minded individuals', 'These individuals', 'qualified individuals', 'certain individuals', 'different individuals', 'For individuals', 'few individuals', 'and individuals', 'two individuals', 'for individuals', 'between individuals', 'affected individuals', 'healthy individuals', 'private individuals', 'with individuals', 'following individuals', 'as individuals', 'such individuals', 'that individuals', 'all individuals', 'of individuals', 'many individuals']
| MIT | ipynb/How to Do Things with Words.ipynb | mikiec84/pytudes |
Intuitively, words that appear in many bigrams before are more likely to appear in a new, previously unseen bigram. In *Kneser-Ney* smoothing (Reinhard Kneser, Hermann Ney) we multiply the bigram counts by this ratio. But I won't implement that here, because The Count never covered it.(10) One More Task: Secret Codes===Let's tackle one more task: decoding secret codes. We'll start with the simplest of codes, a rotation cipher, sometimes called a shift cipher or a Caesar cipher (because this was state-of-the-art crypotgraphy in 100 BC). First, a method to encode: | def rot(msg, n=13):
"Encode a message with a rotation (Caesar) cipher."
return encode(msg, alphabet[n:]+alphabet[:n])
def encode(msg, key):
"Encode a message with a substitution cipher."
table = string.maketrans(upperlower(alphabet), upperlower(key))
return msg.translate(table)
def upperlower(text): return text.upper() + text.lower()
rot('This is a secret message.', 1)
rot('This is a secret message.', 13)
rot(rot('This is a secret message.')) | _____no_output_____ | MIT | ipynb/How to Do Things with Words.ipynb | mikiec84/pytudes |
Now decoding is easy: try all 26 candidates, and find the one with the maximum Pwords: | def decode_rot(secret):
"Decode a secret message that has been encoded with a rotation cipher."
candidates = [rot(secret, i) for i in range(len(alphabet))]
return max(candidates, key=lambda msg: Pwords(tokens(msg)))
msg = 'Who knows the answer?'
secret = rot(msg, 17)
print(secret)
print(decode_rot(secret)) | Nyf befnj kyv rejnvi?
Who knows the answer?
| MIT | ipynb/How to Do Things with Words.ipynb | mikiec84/pytudes |
Let's make it a tiny bit harder. When the secret message contains separate words, it is too easy to decode by guessing that the one-letter words are most likely "I" or "a". So what if the encode routine mushed all the letters together: | def encode(msg, key):
"Encode a message with a substitution cipher; remove non-letters."
msg = cat(tokens(msg)) ## Change here
table = string.maketrans(upperlower(alphabet), upperlower(key))
return msg.translate(table) | _____no_output_____ | MIT | ipynb/How to Do Things with Words.ipynb | mikiec84/pytudes |
Now we can decode by segmenting. We change candidates to be a list of segmentations, and still choose the candidate with the best Pwords: | def decode_rot(secret):
"""Decode a secret message that has been encoded with a rotation cipher,
and which has had all the non-letters squeezed out."""
candidates = [segment(rot(secret, i)) for i in range(len(alphabet))]
return max(candidates, key=lambda msg: Pwords(msg))
msg = 'Who knows the answer this time? Anyone? Bueller?'
secret = rot(msg, 19)
print(secret)
print(decode_rot(secret))
candidates = [segment(rot(secret, i)) for i in range(len(alphabet))]
for c in candidates:
print int(log10(Pwords(c))), ' '.join(c) | -123 p ah d g h p l max t g l p x km a bl mb f x t gr h g x un x e ex k
-133 q b i eh i q m n by u hm q y l n b cm n c g y u h s i h y vo y ff y l
-128 r c j f i jr no c z v in r z mo c d nod h z v it j i z w p z g g z m
-115 sd k g j k so p da w j os an p de op e i a w j u k j ax q ah h an
-144 t el h k l t p q e b x k p t b o q e f p q f j b x k v l k by r b ii b o
-145 u f m il m u q r f cy l qu c p r f g q r g k cy l wm l c z s c j j c p
-141 v g n j m n v r sg d z mr v d q sg h r s h l d z m x nm dat d k k d q
-39 who knows the answer this time anyone bu e ll er
-119 xi p l op x tu if b o t x f s u i j tu j n f b oz p of c v f mm f s
-151 y j q m p q y u v j g c p u y g t v j ku v k o g c pa q pg d w g n n g t
-151 z k r n q r z v w k h d q v z h u w k l v w l p h d q br q he x ho oh u
-97 also r saw x lie r w a iv x l m w x m q i er c s r if y i pp iv
-146 b mt p st b x y m j f s x b j w y m n x y n r j f sd t s j g z j q q j w
-137 c n u q tu cy z n k g ty c k x z no y z os k g t eut k ha k r r k x
-124 do v r u v d z a o l h u z d ly a op z apt l h u f v u li bl s sly
-121 e p w s v we ab pm iv a em z b p q ab qu m iv g w v m j cm t tm z
-142 f q x t w x f bc q n j w b fn a c q r bc r v n j wh x w n k d n u un a
-119 gr y u x y g c dr ok x c go b dr s c d s w ok xi y x o leo v vo b
-119 h s z vy z h des ply d h p c est de t x ply j z y pm f p w w p c
-137 it a w z a i e ft q m ze i q d f tu e f u y q m z ka z q n g q xx q d
-130 j u b x ab j f g u r na f jr eg u v f g v z r n alba r oh r y y re
-125 k v cy bc k g h v sob g k s f h v w g h was o b m c b s p is z z s f
-130 l w d z c d l hi w t p ch l t g i w x h ix b t p c nd c t q j t a at g
-123 m x e a dem i j x u q dim u h j x y i j y c u q doe du r ku b bu h
-124 ny f be fn j k y v re j n vi k y z j k z d v rep fe vs l v c c vi
-134 oz g cf go k l z w s f k o w j l z ak la e w s f q g f w tm w d d w j
| MIT | ipynb/How to Do Things with Words.ipynb | mikiec84/pytudes |
What about a general substitution cipher? The problem is that there are 26! substitution ciphers, and we can't enumerate all of them. We would need to search through this space. Initially make some guess at a substitution, then swap two letters; if that looks better keep going, if not try something else. This approach solves most substitution cipher problems, although it can take a few minutes on a message of length 100 words or so.(∞ and beyond) Where To Go Next===What to do next? Here are some options: - **Spelling correction**: Use bigram or trigram context; make a model of spelling errors/edit distance; go beyond edit distance 2; make it more efficient- **Evaluation**: Make a serious test suite; search for best parameters (e.g. $c_1, c_2, c_3$)- **Smoothing**: Implement Kneser-Ney and/or Interpolation; do letter *n*-gram-based smoothing- **Secret Codes**: Implement a search over substitution ciphers- **Classification**: Given a corpus of texts, each with a classification label, write a classifier that will take a new text and return a label. Examples: spam/no-spam; favorable/unfavorable; what author am I most like; reading level.- **Clustering**: Group data by similarity. Find synonyms/related words.- **Parsing**: Representing nested structures rather than linear sequences of words. relations between parts of the structure. Implicit missing bits. Inducing a grammar.- **Meaning**: What semantic relations are meant by the syntactic relations?- **Translation**: Using examples to transform one language into another.- **Question Answering**: Using examples to transfer a question into an answer, either by retrieving a passage, or by synthesizing one.- **Speech**: Dealing with analog audio signals rather than discrete sequences of characters. | correct('cpgratulations') | _____no_output_____ | MIT | ipynb/How to Do Things with Words.ipynb | mikiec84/pytudes |
Experimenting with smaller action spacesHere we look at the board (states) that all evolve from an initial board. We're going to train on all board snapshots of a series of heuristically played games. | def new_initial_state(heuristics):
board = GomokuBoard(heuristics, N=15, disp_width=8)
policy = HeuristicGomokuPolicy(style = 2, bias=.5, topn=5,
threat_search=ThreatSearch(2,2))
board.set(H,8).set('G',6).set(G,8).set(F,8).set(H,9).set(H,10)
return board, policy
new_initial_state(heuristics)[0].display('current')
board, policy = new_initial_state(heuristics)
_ = roll_out(board, policy, 40)
board.display('current')
stones = board.stones.copy()
from wgomoku import heuristic_QF, transform, create_sample, wrap_sample
sample = create_sample(board.stones.copy(), board.N, 1-board.current_color)
o,d = np.rollaxis(sample, 2, 0)
2 * d + o
def create_samples_and_qvalues(board, heuristics):
all_stones_t = [transform(board.stones.copy(), board.N, rot, ref)
for rot in range(4)
for ref in [False, True]]
samples = []
qvalues = []
for stones_t in all_stones_t:
sample = create_sample(stones_t, board.N, 1-board.current_color)
board = GomokuBoard(heuristics=heuristics, N=board.N, stones=stones_t)
policy = HeuristicGomokuPolicy(style = STYLE_MIXED, bias=.5, topn=5,
threat_search=ThreatSearch(2,2))
qvalue, default_value = heuristic_QF(board, policy)
qvalue = wrap_sample(qvalue, default_value)
samples.append(sample)
qvalues.append(qvalue)
return np.array(samples), np.reshape(qvalues, [8, board.N+2, board.N+2, 1])
samples, values = create_samples_and_qvalues(board, heuristics)
np.shape(samples),np.shape(values)
b, w = np.rollaxis(samples[3], 2, 0)
b + 2*w | _____no_output_____ | Apache-2.0 | GomokuData.ipynb | smurve/DeepGomoku |
Plausibility check | index = np.argmax(values[3])
r, c = np.divmod(index,17)
pos=gt.m2b((r, c), 17)
print(r,c,pos, values[3][r][c])
from wgomoku import create_samples_and_qvalues
def data_from_game(board, policy, heuristics):
"""
Careful: This function rolls back the board
"""
s,v = create_samples_and_qvalues(board, heuristics)
while board.cursor > 6:
board.undo()
s1, v1 = create_samples_and_qvalues(board, heuristics)
s = np.concatenate((s,s1))
v = np.concatenate((v,v1))
return s,v
from wgomoku import data_from_game
data = data_from_game(board, policy, heuristics)
o,d = np.rollaxis(data[0][30], 2, 0)
2 * d + o | _____no_output_____ | Apache-2.0 | GomokuData.ipynb | smurve/DeepGomoku |
Yet another plausibility check | sample_no = 30
index = np.argmax(data[1][sample_no])
r, c = np.divmod(index,17)
pos=gt.m2b((r, c), 17)
print(r,c,pos)
print("Value of the suggested pos:")
print(data[1][sample_no][r][c])
print("Value to the right of the suggested pos:")
print(data[1][sample_no][r][c+1]) | _____no_output_____ | Apache-2.0 | GomokuData.ipynb | smurve/DeepGomoku |
Save the results for later training | np.save("samples.npy", data[0])
np.save("values.npy", data[1])
samples = np.load("samples.npy")
values = np.load("values.npy")
samples.shape, values.shape
np.rollaxis(samples[0], 2, 0)
np.rollaxis(values[0], 2, 0)
np.rollaxis(samples[0], 2, 0)[0] + 2*np.rollaxis(samples[0], 2, 0)[1] | _____no_output_____ | Apache-2.0 | GomokuData.ipynb | smurve/DeepGomoku |
Randomized Benchmarking IntroductionOne of the main challenges in building a quantum information processor is the non-scalability of completelycharacterizing the noise affecting a quantum system via process tomography. In addition, process tomography is sensitive to noise in the pre- and post rotation gates plus the measurements (SPAM errors). Gateset tomography can take these errors into account, but the scaling is even worse. A complete characterizationof the noise is useful because it allows for the determination of good error-correction schemes, and thusthe possibility of reliable transmission of quantum information.Since complete process tomography is infeasible for large systems, there is growing interest in scalablemethods for partially characterizing the noise affecting a quantum system. A scalable (in the number $n$ of qubits comprising the system) and robust algorithm for benchmarking the full set of Clifford gates by a single parameter using randomization techniques was presented in [1]. The concept of using randomization methods for benchmarking quantum gates is commonly called **Randomized Benchmarking(RB)**. References1. Easwar Magesan, J. M. Gambetta, and Joseph Emerson, *Robust randomized benchmarking of quantum processes*,https://arxiv.org/pdf/1009.36392. Easwar Magesan, Jay M. Gambetta, and Joseph Emerson, *Characterizing Quantum Gates via Randomized Benchmarking*,https://arxiv.org/pdf/1109.68873. A. D. C'orcoles, Jay M. Gambetta, Jerry M. Chow, John A. Smolin, Matthew Ware, J. D. Strand, B. L. T. Plourde, and M. Steffen, *Process verification of two-qubit quantum gates by randomized benchmarking*, https://arxiv.org/pdf/1210.70114. Jay M. Gambetta, A. D. C´orcoles, S. T. Merkel, B. R. Johnson, John A. Smolin, Jerry M. Chow,Colm A. Ryan, Chad Rigetti, S. Poletto, Thomas A. Ohki, Mark B. Ketchen, and M. Steffen,*Characterization of addressability by simultaneous randomized benchmarking*, https://arxiv.org/pdf/1204.63085. David C. McKay, Sarah Sheldon, John A. Smolin, Jerry M. Chow, and Jay M. Gambetta, *Three Qubit Randomized Benchmarking*, https://arxiv.org/pdf/1712.06550 The Randomized Benchmarking ProtocolA RB protocol (see [1,2]) consists of the following steps:(We should first import the relevant qiskit classes for the demonstration). | #Import general libraries (needed for functions)
import numpy as np
import matplotlib.pyplot as plt
from IPython import display
#Import the RB Functions
import qiskit.ignis.verification.randomized_benchmarking as rb
#Import Qiskit classes
import qiskit
from qiskit.providers.aer.noise import NoiseModel
from qiskit.providers.aer.noise.errors.standard_errors import depolarizing_error, thermal_relaxation_error | _____no_output_____ | Apache-2.0 | content/ch-quantum-hardware/randomized-benchmarking.ipynb | NunoEdgarGFlowHub/qiskit-textbook |
Step 1: Generate RB sequencesThe RB sequences consist of random Clifford elements chosen uniformly from the Clifford group on $n$-qubits, including a computed reversal element,that should return the qubits to the initial state.More precisely, for each length $m$, we choose $K_m$ RB sequences. Each such sequence contains $m$ random elements $C_{i_j}$ chosen uniformly from the Clifford group on $n$-qubits, and the $m+1$ element is defined as follows: $C_{i_{m+1}} = (C_{i_1}\cdot ... \cdot C_{i_m})^{-1}$. It can be found efficiently by the Gottesmann-Knill theorem.For example, we generate below several sequences of 2-qubit Clifford circuits. | #Generate RB circuits (2Q RB)
#number of qubits
nQ=2
rb_opts = {}
#Number of Cliffords in the sequence
rb_opts['length_vector'] = [1, 10, 20, 50, 75, 100, 125, 150, 175, 200]
#Number of seeds (random sequences)
rb_opts['nseeds'] = 5
#Default pattern
rb_opts['rb_pattern'] = [[0,1]]
rb_circs, xdata = rb.randomized_benchmarking_seq(**rb_opts) | Making the n=2 Clifford Table
| Apache-2.0 | content/ch-quantum-hardware/randomized-benchmarking.ipynb | NunoEdgarGFlowHub/qiskit-textbook |
As an example, we print the circuit corresponding to the first RB sequence | print(rb_circs[0][0]) | ┌───┐┌───┐┌───┐ ░ ┌───┐┌─────┐┌───┐ ┌─┐ »
qr_0: |0>──────────────■──┤ H ├┤ S ├┤ Z ├─░─┤ Z ├┤ Sdg ├┤ H ├──■───────┤M├─────»
┌─────┐┌───┐┌─┴─┐├───┤├───┤├───┤ ░ ├───┤├─────┤├───┤┌─┴─┐┌───┐└╥┘┌───┐»
qr_1: |0>┤ Sdg ├┤ H ├┤ X ├┤ H ├┤ S ├┤ Z ├─░─┤ Z ├┤ Sdg ├┤ H ├┤ X ├┤ H ├─╫─┤ S ├»
└─────┘└───┘└───┘└───┘└───┘└───┘ ░ └───┘└─────┘└───┘└───┘└───┘ ║ └───┘»
cr_0: 0 ═══════════════════════════════════════════════════════════════╩══════»
»
cr_1: 0 ══════════════════════════════════════════════════════════════════════»
»
«
«qr_0: ───
« ┌─┐
«qr_1: ┤M├
« └╥┘
«cr_0: ═╬═
« ║
«cr_1: ═╩═
«
| Apache-2.0 | content/ch-quantum-hardware/randomized-benchmarking.ipynb | NunoEdgarGFlowHub/qiskit-textbook |
One can verify that the Unitary representing each RB circuit should be the identity (with a global phase). We simulate this using Aer unitary simulator. | # Create a new circuit without the measurement
qregs = rb_circs[0][-1].qregs
cregs = rb_circs[0][-1].cregs
qc = qiskit.QuantumCircuit(*qregs, *cregs)
for i in rb_circs[0][-1][0:-nQ]:
qc.data.append(i)
# The Unitary is an identity (with a global phase)
backend = qiskit.Aer.get_backend('unitary_simulator')
basis_gates = ['u1','u2','u3','cx'] # use U,CX for now
job = qiskit.execute(qc, backend=backend, basis_gates=basis_gates)
print(np.around(job.result().get_unitary(),3)) | [[ 0.+1.j -0.+0.j -0.+0.j -0.+0.j]
[-0.+0.j 0.+1.j -0.-0.j 0.+0.j]
[ 0.-0.j 0.+0.j 0.+1.j -0.+0.j]
[-0.-0.j 0.-0.j 0.-0.j 0.+1.j]]
| Apache-2.0 | content/ch-quantum-hardware/randomized-benchmarking.ipynb | NunoEdgarGFlowHub/qiskit-textbook |
Step 2: Execute the RB sequences (with some noise)We can execute the RB sequences either using Qiskit Aer Simulator (with some noise model) or using IBMQ provider, and obtain a list of results.By assumption each operation $C_{i_j}$ is allowed to have some error, represented by $\Lambda_{i_j,j}$, and each sequence can be modeled by the operation:$$\textit{S}_{\textbf{i}_\textbf{m}} = \bigcirc_{j=1}^{m+1} (\Lambda_{i_j,j} \circ C_{i_j})$$where ${\textbf{i}_\textbf{m}} = (i_1,...,i_m)$ and $i_{m+1}$ is uniquely determined by ${\textbf{i}_\textbf{m}}$. | # Run on a noisy simulator
noise_model = NoiseModel()
# Depolarizing_error
dp = 0.005
noise_model.add_all_qubit_quantum_error(depolarizing_error(dp, 1), ['u1', 'u2', 'u3'])
noise_model.add_all_qubit_quantum_error(depolarizing_error(2*dp, 2), 'cx')
backend = qiskit.Aer.get_backend('qasm_simulator') | _____no_output_____ | Apache-2.0 | content/ch-quantum-hardware/randomized-benchmarking.ipynb | NunoEdgarGFlowHub/qiskit-textbook |
Step 3: Get statistics about the survival probabilitiesFor each of the $K_m$ sequences the survival probability $Tr[E_\psi \textit{S}_{\textbf{i}_\textbf{m}}(\rho_\psi)]$is measured. Here $\rho_\psi$ is the initial state taking into account preparation errors and $E_\psi$ is thePOVM element that takes into account measurement errors.In the ideal (noise-free) case $\rho_\psi = E_\psi = | \psi {\rangle} {\langle} \psi |$. In practice one can measure the probability to go back to the exact initial state, i.e. all the qubits in the ground state $ {|} 00...0 {\rangle}$ or just the probability for one of the qubits to return back to the ground state. Measuring the qubits independently can be more convenient if a correlated measurement scheme is not possible. Both measurements will fit to the same decay parameter according to the properties of the *twirl*. Step 4: Find the averaged sequence fidelityAverage over the $K_m$ random realizations of the sequence to find the averaged sequence **fidelity**,$$F_{seq}(m,|\psi{\rangle}) = Tr[E_\psi \textit{S}_{K_m}(\rho_\psi)]$$where $$\textit{S}_{K_m} = \frac{1}{K_m} \sum_{\textbf{i}_\textbf{m}} \textit{S}_{\textbf{i}_\textbf{m}}$$is the average sequence operation. Step 5: Fit the resultsRepeat Steps 1 through 4 for different values of $m$ and fit the results for the averaged sequence fidelity to the model:$$ \textit{F}_{seq}^{(0)} \big(m,{|}\psi {\rangle} \big) = A_0 \alpha^m +B_0$$where $A_0$ and $B_0$ absorb state preparation and measurement errors as well as an edge effect from theerror on the final gate.$\alpha$ determines the average error-rate $r$, which is also called **Error per Clifford (EPC)** according to the relation $$ r = 1-\alpha-\frac{1-\alpha}{2^n} = \frac{2^n-1}{2^n}(1-\alpha)$$(where $n=nQ$ is the number of qubits).As an example, we calculate the average sequence fidelity for each of the RB sequences, fit the results to the exponential curve, and compute the parameters $\alpha$ and EPC. | # Create the RB fitter
backend = qiskit.Aer.get_backend('qasm_simulator')
basis_gates = ['u1','u2','u3','cx']
shots = 200
qobj_list = []
rb_fit = rb.RBFitter(None, xdata, rb_opts['rb_pattern'])
for rb_seed,rb_circ_seed in enumerate(rb_circs):
print('Compiling seed %d'%rb_seed)
new_rb_circ_seed = qiskit.compiler.transpile(rb_circ_seed, basis_gates=basis_gates)
qobj = qiskit.compiler.assemble(new_rb_circ_seed, shots=shots)
print('Simulating seed %d'%rb_seed)
job = backend.run(qobj, noise_model=noise_model, backend_options={'max_parallel_experiments': 0})
qobj_list.append(qobj)
# Add data to the fitter
rb_fit.add_data(job.result())
print('After seed %d, alpha: %f, EPC: %f'%(rb_seed,rb_fit.fit[0]['params'][1], rb_fit.fit[0]['epc'])) | Compiling seed 0
| Apache-2.0 | content/ch-quantum-hardware/randomized-benchmarking.ipynb | NunoEdgarGFlowHub/qiskit-textbook |
Plot the results | plt.figure(figsize=(8, 6))
ax = plt.subplot(1, 1, 1)
# Plot the essence by calling plot_rb_data
rb_fit.plot_rb_data(0, ax=ax, add_label=True, show_plt=False)
# Add title and label
ax.set_title('%d Qubit RB'%(nQ), fontsize=18)
plt.show() | _____no_output_____ | Apache-2.0 | content/ch-quantum-hardware/randomized-benchmarking.ipynb | NunoEdgarGFlowHub/qiskit-textbook |
The intuition behind RBThe depolarizing quantum channel has a parameter $\alpha$, and works like this: with probability $\alpha$, the state remains the same as before; with probability $1-\alpha$, the state becomes the totally mixed state, namely:$$\rho_f = \alpha \rho_i + \frac{1-\alpha}{2^n} * \mathbf{I}$$Suppose that we have a sequence of $m$ gates, not necessarily Clifford gates, where the error channel of the gates is a depolarizing channel with parameter $\alpha$ (same $\alpha$ for all the gates). Then with probability $\alpha^m$ the state is correct at the end of the sequence, and with probability $1-\alpha^m$ it becomes the totally mixed state, therefore:$$\rho_f^m = \alpha^m \rho_i + \frac{1-\alpha^m}{2^n} * \mathbf{I}$$Now suppose that in addition we start with the ground state; that the entire sequence amounts to the identity; and that we measure the state at the end of the sequence with the standard basis. We derive that the probability of success at the end of the sequence is:$$\alpha^m + \frac{1-\alpha^m}{2^n} = \frac{2^n-1}{2^n}\alpha^m + \frac{1}{2^n} = A_0\alpha^m + B_0$$It follows that the probability of success, aka fidelity, decays exponentially with the sequence length, with exponent $\alpha$.The last statement is not necessarily true when the channel is other than the depolarizing channel. However, it turns out that if the gates are uniformly-randomized Clifford gates, then the noise of each gate behaves on average as if it was the depolarizing channel, with some parameter that can be computed from the channel, and we obtain the exponential decay of the fidelity.Formally, taking an average over a finite group $G$ (like the Clifford group) of a quantum channel $\bar \Lambda$ is also called a *twirl*:$$ W_G(\bar \Lambda) \frac{1}{|G|} \sum_{u \in G} U^{\dagger} \circ \bar \Lambda \circ U$$Twirling over the entire unitary group yields exactly the same result as the Clifford group. The Clifford group is a *2-design* of the unitary group. Simultaneous Randomized BenchmarkingRB is designed to address fidelities in multiqubit systems in two ways. For one, RB over the full $n$-qubit spacecan be performed by constructing sequences from the $n$-qubit Clifford group. Additionally, the $n$-qubit spacecan be subdivided into sets of qubits $\{n_i\}$ and $n_i$-qubit RB performed in each subset simultaneously [4]. Both methods give metrics of fidelity in the $n$-qubit space. For example, it is common to perform 2Q RB on the subset of two-qubits defining a CNOT gate while the other qubits are quiescent. As explained in [4], this RB data will not necessarily decay exponentially because the other qubit subspaces are not twirled. Subsets are more rigorously characterized by simultaneous RB, which also measures some level of crosstalk error since all qubits are active.An example of simultaneous RB (1Q RB and 2Q RB) can be found in: https://github.com/Qiskit/qiskit-tutorials/blob/master/qiskit/ignis/randomized_benchmarking.ipynb Predicted Gate FidelityIf we know the errors on the underlying gates (the gateset) we can predict the fidelity. First we need to count the number of these gates per Clifford. Then, the two qubit Clifford gate error function gives the error per 2Q Clifford. It assumes that the error in the underlying gates is depolarizing. This function is derived in the supplement to [5]. | #Count the number of single and 2Q gates in the 2Q Cliffords
gates_per_cliff = rb.rb_utils.gates_per_clifford(qobj_list, xdata[0],basis_gates, rb_opts['rb_pattern'][0])
for i in range(len(basis_gates)):
print("Number of %s gates per Clifford: %f"%(basis_gates[i],
np.mean([gates_per_cliff[0][i],gates_per_cliff[1][i]])))
# Prepare lists of the number of qubits and the errors
ngates = np.zeros(7)
ngates[0:3] = gates_per_cliff[0][0:3]
ngates[3:6] = gates_per_cliff[1][0:3]
ngates[6] = gates_per_cliff[0][3]
gate_qubits = np.array([0, 0, 0, 1, 1, 1, -1], dtype=int)
gate_errs = np.zeros(len(gate_qubits))
gate_errs[[1, 4]] = dp/2 #convert from depolarizing error to epg (1Q)
gate_errs[[2, 5]] = 2*dp/2 #convert from depolarizing error to epg (1Q)
gate_errs[6] = dp*3/4 #convert from depolarizing error to epg (2Q)
#Calculate the predicted epc
pred_epc = rb.rb_utils.twoQ_clifford_error(ngates,gate_qubits,gate_errs)
print("Predicted 2Q Error per Clifford: %e"%pred_epc)
import qiskit
qiskit.__qiskit_version__ | _____no_output_____ | Apache-2.0 | content/ch-quantum-hardware/randomized-benchmarking.ipynb | NunoEdgarGFlowHub/qiskit-textbook |
Homework for week 4 Functions 1. Convert String Number to IntegerCreate a function that can take something like ```"$5,356"``` or ```"$250,000"``` or even ```"$10.75"``` and covert into integers like return ```5356```, ```250000``` and ```11``` respectively. | ## build function here
## call the function on "$5,356"
## Did you get 5356?
## call the function on "$10.75"
## Did you get 11?
## Use map() to run your function on the following list
## save result in a list called updated_list
my_amounts = ["$12.24", "$4,576.33", "$23,629.01"] | _____no_output_____ | MIT | homework/homework-for-week-4-BLANKS.ipynb | jchapamalacara/fall21-students-practical-python |
3. What if we encounter a list that has a mix of numbers as strings and integers and floats? | ## Run this cell
more_amounts = [960, "$12.24", "$4,576.33", "$23,629.01", 23.656]
## tweaked the function here to handle all situations
## run it on more_amounts using map()
| _____no_output_____ | MIT | homework/homework-for-week-4-BLANKS.ipynb | jchapamalacara/fall21-students-practical-python |
Compare the outer envelope stellar mass and SDSS redMaPPer clusters DSigma profiles of HSC massive galaxies | # DeltaSigma profiles of HSC massive galaxies
topn_massive = pickle.load(open(os.path.join(res_dir, 'topn_galaxies_sum.pkl'), 'rb'))
# DeltaSigma profiles of redMaPPer and CAMIRA clusters
topn_cluster = pickle.load(open(os.path.join(res_dir, 'topn_clusters_cen_sum.pkl'), 'rb'))
# For clusters, but using both central and satellite galaxies
topn_cluster_all = pickle.load(open(os.path.join(res_dir, 'topn_clusters_sum.pkl'), 'rb')) | _____no_output_____ | MIT | notebooks/figure/figF1.ipynb | mattkwiecien/jianbing |
DSigma profiles of mock galaxies | sim_dsig = Table.read(os.path.join(sim_dir, 'sim_merge_all_dsig.fits')) | _____no_output_____ | MIT | notebooks/figure/figF1.ipynb | mattkwiecien/jianbing |
Halo mass distributions | sim_mhalo = Table.read(os.path.join(sim_dir, 'sim_merge_mhalo_hist.fits')) | _____no_output_____ | MIT | notebooks/figure/figF1.ipynb | mattkwiecien/jianbing |
Pre-compute lensing results for HSC galaxies | # Pre-compute
s16a_precompute = os.path.join(data_dir, 'topn_public_s16a_medium_precompute.hdf5')
hsc_pre = Table.read(s16a_precompute, path='hsc_extra')
red_sdss = Table.read(s16a_precompute, path='redm_sdss')
red_hsc = Table.read(s16a_precompute, path='redm_hsc') | _____no_output_____ | MIT | notebooks/figure/figF1.ipynb | mattkwiecien/jianbing |
Pre-compute lensing results for randoms | # Lensing data using medium photo-z quality cut
s16a_lensing = os.path.join(data_dir, 's16a_weak_lensing_medium.hdf5')
# Random
s16a_rand = Table.read(s16a_lensing, path='random') | _____no_output_____ | MIT | notebooks/figure/figF1.ipynb | mattkwiecien/jianbing |
Pre-defined number density bins | topn_bins = Table.read(os.path.join(bin_dir, 'topn_bins.fits')) | _____no_output_____ | MIT | notebooks/figure/figF1.ipynb | mattkwiecien/jianbing |
Compute the DSigma profiles of SDSS redMaPPer clusters- 0.2 < z < 0.38 clusters | plt.scatter(red_sdss['z_best'], red_sdss['lambda_cluster_redm'], s=5, alpha=0.3)
mask_sdss_1 = (red_sdss['z_best'] >= 0.19) & (red_sdss['z_best'] <= 0.35)
print(mask_sdss_1.sum())
sdss_bin_1 = Table(copy.deepcopy(topn_bins[0]))
sdss_bin_1['n_obj'] = mask_sdss_1.sum()
sdss_bin_1['rank_low'] = 1
sdss_bin_1['rank_upp'] = mask_sdss_1.sum()
sdss_bin_1['index_low'] = 0
sdss_bin_1['index_upp'] = mask_sdss_1.sum() - 1
mask_sdss_2 = (red_sdss['z_best'] >= 0.19) & (red_sdss['z_best'] <= 0.50) & (red_sdss['lambda_cluster_redm'] >= 50)
print(mask_sdss_2.sum())
sdss_bin_2 = Table(copy.deepcopy(topn_bins[0]))
sdss_bin_2['n_obj'] = mask_sdss_2.sum()
sdss_bin_2['rank_low'] = 1
sdss_bin_2['rank_upp'] = mask_sdss_2.sum()
sdss_bin_2['index_low'] = 0
sdss_bin_2['index_upp'] = mask_sdss_2.sum() - 1
redm_sdss_dsig_1 = wlensing.gather_topn_dsigma_profiles(
red_sdss, s16a_rand, sdss_bin_1, 'lambda_cluster_redm',
mask=mask_sdss_1, n_rand=100000, n_boot=200, verbose=True)
redm_sdss_sum_1 = scatter.compare_model_dsigma(
redm_sdss_dsig_1, sim_dsig, model_err=False, poly=True, verbose=True)
fig = visual.sum_plot_topn(
redm_sdss_sum_1, r'$\lambda_{\rm SDSS}$', note=None, cov_type='jk', ref_tab=None)
redm_sdss_dsig_2 = wlensing.gather_topn_dsigma_profiles(
red_sdss, s16a_rand, sdss_bin_2, 'lambda_cluster_redm',
mask=mask_sdss_2, n_rand=100000, n_boot=200, verbose=True)
redm_sdss_sum_2 = scatter.compare_model_dsigma(
redm_sdss_dsig_2, sim_dsig, model_err=False, poly=True, verbose=True)
fig = visual.sum_plot_topn(
redm_sdss_sum_2, r'$\lambda_{\rm SDSS}$', note=None, cov_type='jk', ref_tab=None) |
# Using column: lambda_cluster_redm
# Bin 1: 0 - 54
# Dealing with Bin: 1
| MIT | notebooks/figure/figF1.ipynb | mattkwiecien/jianbing |
Compute the DSigma profiles of HSC redMaPPer clusters | plt.scatter(red_hsc['z_best'], red_hsc['lambda'], s=5, alpha=0.3)
mask_hsc_1 = (red_hsc['z_best'] >= 0.19) & (red_hsc['z_best'] <= 0.35)
redm_hsc_dsig_1 = wlensing.gather_topn_dsigma_profiles(
red_hsc, s16a_rand, sdss_bin_1, 'lambda',
mask=mask_hsc_1, n_rand=100000, n_boot=200, verbose=True)
print(np.min(redm_hsc_dsig['samples']))
redm_hsc_sum_1 = scatter.compare_model_dsigma(
redm_hsc_dsig_1, sim_dsig, model_err=False, poly=True, verbose=True)
fig = visual.sum_plot_topn(
redm_hsc_sum_1, r'$\lambda_{\rm HSC}$', note=None, cov_type='jk', ref_tab=None)
mask_hsc_2 = (red_hsc['z_best'] >= 0.19) & (red_hsc['z_best'] <= 0.5)
redm_hsc_dsig_2 = wlensing.gather_topn_dsigma_profiles(
red_hsc, s16a_rand, sdss_bin_2, 'lambda',
mask=mask_hsc_2, n_rand=100000, n_boot=200, verbose=True)
print(np.min(redm_hsc_dsig['samples']))
redm_hsc_sum_2 = scatter.compare_model_dsigma(
redm_hsc_dsig_2, sim_dsig, model_err=False, poly=True, verbose=True)
fig = visual.sum_plot_topn(
redm_hsc_sum_2, r'$\lambda_{\rm HSC}$', note=None, cov_type='jk', ref_tab=None) |
# Using column: lambda
# Bin 1: 0 - 54
14.115065
# Dealing with Bin: 1
| MIT | notebooks/figure/figF1.ipynb | mattkwiecien/jianbing |
Compute the DSigma profiles of HSC massive galaxies | # S18A bright star mask
bsm_s18a = hsc_pre['flag'] > 0
# General mask for HSC galaxies
mask_mout_1 = (
(hsc_pre['c82_100'] <= 18.) & (hsc_pre['logm_100'] - hsc_pre['logm_50'] <= 0.2) & (hsc_pre['logm_50_100'] > 0) &
bsm_s18a & (hsc_pre['z'] >= 0.19) & (hsc_pre['z'] <= 0.35)
)
mask_mout_2 = (
(hsc_pre['c82_100'] <= 18.) & (hsc_pre['logm_100'] - hsc_pre['logm_50'] <= 0.2) & (hsc_pre['logm_50_100'] > 0) &
bsm_s18a & (hsc_pre['z'] >= 0.19) & (hsc_pre['z'] <= 0.50)
)
# Mask to select "central" galaxies
cen_mask_1 = hsc_pre['cen_mask_1'] > 0
cen_mask_2 = hsc_pre['cen_mask_2'] > 0
cen_mask_3 = hsc_pre['cen_mask_3'] > 0
hsc_mout_dsig_1 = wlensing.gather_topn_dsigma_profiles(
hsc_pre, s16a_rand, sdss_bin_1, 'logm_50_100', mask=mask_mout_1,
n_rand=100000, n_boot=200, verbose=True)
hsc_mout_sum_1 = scatter.compare_model_dsigma(
hsc_mout_dsig_1, sim_dsig, model_err=False, poly=True, verbose=True)
fig = visual.sum_plot_topn(
hsc_mout_sum_1, r'$M_{\star, [50,100]}$', note=None, cov_type='jk', ref_tab=None)
hsc_mout_dsig_2 = wlensing.gather_topn_dsigma_profiles(
hsc_pre, s16a_rand, sdss_bin_2, 'logm_50_100', mask=mask_mout_2,
n_rand=100000, n_boot=200, verbose=True)
hsc_mout_sum_2 = scatter.compare_model_dsigma(
hsc_mout_dsig_2, sim_dsig, model_err=False, poly=True, verbose=True)
fig = visual.sum_plot_topn(
hsc_mout_sum_2, r'$M_{\star, [50,100]}$', note=None, cov_type='jk', ref_tab=None) |
# Using column: logm_50_100
# Bin 1: 0 - 54
# Dealing with Bin: 1
| MIT | notebooks/figure/figF1.ipynb | mattkwiecien/jianbing |
Making a figure for the paper | def compare_sdss_redm_profiles(
dsig_ref, dsig_cmp, sim_dsig, sig_type='bt', compare_to_model=True,
label_ref=r'$\rm Ref$', label_cmp=r'$\rm Test$',
sub_ref=r'{\rm Ref}', sub_cmp=r'{\rm Test}',
cmap_list=None, color_bins=None,
marker_ref='o', msize_ref=180,
marker_cmp='P', msize_cmp=180,
show_best_cmp=False, middle_title=None, mvir_min=None,
dsig_cmp_2=None, label_cmp_2=r'$=rm Test 2$'):
"""Compare the Dsigma profiles."""
def get_dsig_ratio(obs, ref, mod=None):
""""""
obs_rand = np.random.normal(
loc=obs['dsigma'][0], scale=obs['dsig_err_{:s}'.format(sig_type)][0])
if mod is not None:
ref_rand = np.random.normal(
loc=mod['dsig'], scale=(mod['dsig_err'] * err_factor))
ref_inter = 10.0 ** (
interpolate.interp1d(
mod['r_mpc'], np.log10(ref_rand), fill_value='extrapolate')(r_mpc_obs)
)
return obs_rand / ref_inter
else:
ref_rand = np.random.normal(
loc=ref['dsigma'][0], scale=obs['dsig_err_{:s}'.format(sig_type)][0])
return obs_rand / ref_rand
# Color maps and bins
if cmap_list is None:
cmap_list = [
palettable.colorbrewer.sequential.OrRd_7_r,
palettable.colorbrewer.sequential.Blues_7_r,
palettable.colorbrewer.sequential.YlGn_7_r,
palettable.colorbrewer.sequential.Purples_7_r]
if color_bins is None:
color_bins = ["#e41a1c", "#377eb8", "#1b9e77", "#984ea3"]
# Radius bin of the observed DSigma profiles
r_mpc_obs = dsig_ref[0].meta['r_mpc']
# ---- Start the figure ---- #
# Setup the figure
n_col, n_bins = 2, len(dsig_ref)
fig_y = int(4.2 * n_bins + 2)
left, right = 0.11, 0.995
if n_bins == 4:
bottom, top = 0.055, 0.96
elif n_bins == 3:
bottom, top = 0.08, 0.96
elif n_bins == 2:
bottom, top = 0.10, 0.93
elif n_bins == 1:
bottom, top = 0.16, 0.92
x_space = 0.12
x_size = (right - left - x_space * 1.05) / n_col
y_size = (top - bottom) / n_bins
fig = plt.figure(figsize=(12, fig_y))
for idx, dsig in enumerate(dsig_ref):
# Setup the three columns
ax1 = fig.add_axes([left, top - y_size * (idx + 1), x_size, y_size])
ax2 = fig.add_axes([left + x_space + x_size, top - y_size * (idx + 1), x_size, y_size])
# Subplot title
if idx == 0:
ax1.set_title(r'$R \times \Delta\Sigma\ \rm Profile$', fontsize=35, pad=10)
if middle_title is None:
ax2.set_title(r'${\rm Richness\ v.s.}\ M_{\star,\ \rm Outer}$', fontsize=35, pad=10)
else:
ax2.set_title(middle_title, fontsize=35, pad=10)
# Color map
cmap, color = cmap_list[idx], color_bins[idx]
# MDPL halo mass information for this bin
sim_dsig_bin = sim_dsig[sim_dsig['bin'] == 0]
# DSigma result for this bin
dsig_ref_bin = dsig_ref[idx]
dsig_cmp_bin = dsig_cmp[idx]
# Best fit DSigma profiles
dsig_ref_best = sim_dsig_bin[
np.argmin(
np.abs(sim_dsig_bin['scatter'] - dsig_ref_bin['sig_med_{:s}'.format(sig_type)]))]
dsig_cmp_best = sim_dsig_bin[
np.argmin(
np.abs(sim_dsig_bin['scatter'] - dsig_cmp_bin['sig_med_{:s}'.format(sig_type)]))]
if dsig_ref_bin['sig_med_{:s}'.format(sig_type)] < 0.6:
err_factor = 4.
else:
err_factor = 3.
# Interpolated the reference model profile
ref_model_inter = 10.0 ** (
interpolate.interp1d(
dsig_ref_best['r_mpc'], np.log10(dsig_ref_best['dsig']),
fill_value='extrapolate')(r_mpc_obs)
)
if compare_to_model:
ratio_sample = [
get_dsig_ratio(
dsig_cmp_bin, dsig_ref_bin, mod=dsig_ref_best) for i in np.arange(2000)]
ratio_cmp = dsig_cmp_bin['dsigma'][0] / ref_model_inter
else:
ratio_sample = [
get_dsig_ratio(dsig_cmp_bin, dsig_ref_bin, mod=None) for i in np.arange(2000)]
ratio_cmp = dsig_cmp_bin['dsigma'][0] / dsig_ref_bin['dsigma'][0]
ratio_cmp_err_low = ratio_cmp - np.nanpercentile(ratio_sample, 16, axis=0)
ratio_cmp_err_upp = np.nanpercentile(ratio_sample, 84, axis=0) - ratio_cmp
if dsig_cmp_2 is not None:
try:
dsig_cmp_2_bin = dsig_cmp_2[idx]
dsig_cmp_2_best = sim_dsig_bin[
np.argmin(
np.abs(sim_dsig_bin['scatter'] - dsig_cmp_2_bin['sig_med_{:s}'.format(sig_type)]))]
if compare_to_model:
ratio_sample = [
get_dsig_ratio(
dsig_cmp_2_bin, dsig_ref_bin, mod=dsig_ref_best) for i in np.arange(2000)]
ratio_cmp_2 = dsig_cmp_2_bin['dsigma'][0] / ref_model_inter
else:
ratio_sample = [
get_dsig_ratio(dsig_cmp_2_bin, dsig_ref_bin, mod=None) for i in np.arange(2000)]
ratio_cmp_2 = dsig_cmp_2_bin['dsigma'][0] / dsig_ref_bin['dsigma'][0]
ratio_cmp_2_err_low = ratio_cmp_2 - np.nanpercentile(ratio_sample, 16, axis=0)
ratio_cmp_2_err_upp = np.nanpercentile(ratio_sample, 84, axis=0) - ratio_cmp_2
show_cmp_2 = True
except Exception:
show_cmp_2 = False
else:
show_cmp_2 = False
# ----- Plot 1: R x DSigma plot ----- #
ax1.set_xscale("log", nonpositive='clip')
# MDPL: Best-fit
ax1.fill_between(
dsig_ref_best['r_mpc'],
dsig_ref_best['r_mpc'] * (
dsig_ref_best['dsig'] - dsig_ref_best['dsig_err'] * err_factor),
dsig_ref_best['r_mpc'] * (
dsig_ref_best['dsig'] + dsig_ref_best['dsig_err'] * err_factor),
alpha=0.2, edgecolor='grey', linewidth=2.0,
label=r'__no_label__', facecolor='grey', linestyle='-', rasterized=True)
if show_best_cmp:
ax1.fill_between(
dsig_cmp_best['r_mpc'],
dsig_cmp_best['r_mpc'] * (
dsig_cmp_best['dsig'] - dsig_cmp_best['dsig_err'] * err_factor),
dsig_cmp_best['r_mpc'] * (
dsig_cmp_best['dsig'] + dsig_cmp_best['dsig_err'] * err_factor),
alpha=0.15, edgecolor='grey', linewidth=2.0,
label=r'__no_label__', facecolor='grey', linestyle='--', rasterized=True)
# Reference DSigma profile
ax1.errorbar(
r_mpc_obs,
r_mpc_obs * dsig_ref_bin['dsigma'][0],
yerr=(r_mpc_obs * dsig_ref_bin['dsig_err_{:s}'.format(sig_type)][0]),
ecolor=cmap.mpl_colormap(0.6), color=cmap.mpl_colormap(0.6), alpha=0.9,
capsize=4, capthick=2.5, elinewidth=2.5,
label='__no_label__', fmt='o', zorder=0)
ax1.scatter(
r_mpc_obs,
r_mpc_obs * dsig_ref_bin['dsigma'][0],
s=msize_ref, alpha=0.9, facecolor=cmap.mpl_colormap(0.6), edgecolor='w', marker=marker_ref,
linewidth=2.5, label=label_ref)
# DSigma profiles to compare with
ax1.errorbar(
r_mpc_obs * 1.01,
r_mpc_obs * dsig_cmp_bin['dsigma'][0],
yerr=(r_mpc_obs * dsig_cmp_bin['dsig_err_{:s}'.format(sig_type)][0]),
ecolor=color, color='w', alpha=0.9, capsize=4, capthick=2.5,
elinewidth=2.5, label='__no_label__', fmt='o', zorder=0)
ax1.scatter(
r_mpc_obs * 1.01,
r_mpc_obs * dsig_cmp_bin['dsigma'][0],
s=msize_cmp, alpha=0.95, facecolor='w', edgecolor=color,
marker=marker_cmp, linewidth=3.0, label=label_cmp)
y_max = np.max(
[np.max(dsig_ref_best['r_mpc'] * dsig_ref_best['dsig']),
np.max(dsig_cmp_best['r_mpc'] * dsig_cmp_best['dsig'])]) * 1.47
ax1.set_ylim(3.1, y_max)
# Sample Info
if idx == 1:
_ = ax1.text(0.2, 0.08, r'$0.19 < z < 0.35$', fontsize=28, transform=ax1.transAxes)
_ = ax2.text(0.16, 0.08, r'$\lambda_{\rm SDSS} \geq 20;\ N=191$', fontsize=28, transform=ax2.transAxes)
elif idx == 0:
_ = ax1.text(0.2, 0.08, r'$0.19 < z < 0.50$', fontsize=28, transform=ax1.transAxes)
_ = ax2.text(0.16, 0.08, r'$\lambda_{\rm SDSS} \geq 50;\ N=55$', fontsize=28, transform=ax2.transAxes)
if idx == 1:
ax1.legend(loc='upper left', fontsize=22, handletextpad=0.04, ncol=2, mode="expand")
if idx == len(dsig_ref) - 1:
_ = ax1.set_xlabel(r'$R\ [\mathrm{Mpc}]$', fontsize=30)
else:
ax1.set_xticklabels([])
_ = ax1.set_ylabel(r'$R \times \Delta\Sigma\ [10^{6}\ M_{\odot}/\mathrm{pc}]$', fontsize=32)
# ----- Plot 2: Ratio of DSigma plot ----- #
ax2.set_xscale("log", nonpositive='clip')
ax2.axhline(
1.0, linewidth=3.0, alpha=0.5, color='k', linestyle='--', label='__no_label__', )
# Uncertainty of the model
ax2.fill_between(
dsig_ref_best['r_mpc'],
1.0 - (dsig_ref_best['dsig_err'] * err_factor / dsig_ref_best['dsig']),
1.0 + (dsig_ref_best['dsig_err'] * err_factor / dsig_ref_best['dsig']),
alpha=0.2, edgecolor='none', linewidth=1.0, label='__no_label__',
facecolor='grey', rasterized=True)
if show_cmp_2:
ax2.errorbar(
r_mpc_obs * 1.2, ratio_cmp_2, yerr=[ratio_cmp_2_err_low, ratio_cmp_2_err_upp],
ecolor=cmap.mpl_colormap(0.3), color='w', alpha=0.5, capsize=4, capthick=2.5,
elinewidth=3.0, label='__no_label__', fmt='o', zorder=0)
ax2.scatter(
r_mpc_obs * 1.2, ratio_cmp_2,
s=260, alpha=0.7, facecolor=cmap.mpl_colormap(0.3), edgecolor='w',
marker='H', linewidth=3.0, label=label_cmp_2)
ax2.errorbar(
r_mpc_obs, ratio_cmp, yerr=[ratio_cmp_err_low, ratio_cmp_err_upp],
ecolor=color, color='w', alpha=0.8, capsize=4, capthick=2.5,
elinewidth=3.0, label='__no_label__', fmt='o', zorder=0)
ax2.scatter(
r_mpc_obs, ratio_cmp,
s=msize_cmp, alpha=0.9, facecolor='w', edgecolor=color,
marker=marker_cmp, linewidth=3.0, label=label_cmp)
ax2.set_ylim(0.20, 2.49)
if np.max(ratio_cmp) < 1.2:
y_pos = 0.85
else:
y_pos = 0.15
if idx == 1:
ax2.legend(loc='upper left', fontsize=22, handletextpad=0.05)
if idx == len(dsig_ref) - 1:
_ = ax2.set_xlabel(r'$R\ [\mathrm{Mpc}]$', fontsize=30)
else:
ax2.set_xticklabels([])
_ = ax2.set_ylabel(r'$\Delta\Sigma_{\rm redM}/\Delta\Sigma_{[50, 100]}$', fontsize=35)
for tick in ax1.xaxis.get_major_ticks():
tick.label.set_fontsize(30)
for tick in ax1.yaxis.get_major_ticks():
tick.label.set_fontsize(30)
for tick in ax2.xaxis.get_major_ticks():
tick.label.set_fontsize(30)
for tick in ax2.yaxis.get_major_ticks():
tick.label.set_fontsize(30)
return fig
dsig_cmp_2 = [redm_hsc_sum_2, redm_hsc_sum_1]
label_cmp_2 = r'${\rm redM\ HSC}$'
dsig_cmp = [redm_sdss_sum_2, redm_sdss_sum_1]
label_cmp = r'${\rm redM\ SDSS}$'
sub_cmp = r'{\rm redM\ SDSS}z'
dsig_ref = [hsc_mout_sum_2, hsc_mout_sum_1]
label_ref = r'$M_{\star, [50, 100]}$'
sub_ref = r'{[50, 100]}'
fig = compare_sdss_redm_profiles(
dsig_ref, dsig_cmp, sim_dsig, sig_type='bt', compare_to_model=True,
label_ref=label_ref, label_cmp=label_cmp, sub_ref=sub_ref, sub_cmp=sub_cmp,
marker_ref='o', marker_cmp='D', msize_ref=220, msize_cmp=160,
dsig_cmp_2=dsig_cmp_2, label_cmp_2=label_cmp_2, mvir_min=12.8,
middle_title=r'$\rm HSC\ v.s.\ SDSS$')
fig.savefig(os.path.join(fig_dir, 'fig_F1.png'), dpi=120)
fig.savefig(os.path.join(fig_dir, 'fig_F1.pdf'), dpi=120)
redm_hsc_dsig_1['samples'].min()
redm_hsc_dsig_2['samples'].min() | _____no_output_____ | MIT | notebooks/figure/figF1.ipynb | mattkwiecien/jianbing |
Pre-procesamiento de datosTodos los datos operados por un algoritmo de inteligencia artificial deben ser numéricos en una forma específica. Debido a que la mayoría de datos se encuentran en un formato diferente, que no puede ser utilizado en un algoritmo, estos deben ser convertidos a un formato adecuado. Esta tarea se conoce como `preprocessing`. | import pandas as pd
from sklearn.preprocessing import LabelEncoder
# Leer los datos del archivo CSV original
df = pd.read_csv('telco.csv')
df.info()
# Revisar la composición de datos en el DataFrame
df.head() | _____no_output_____ | MIT | clase4/4-1_preprocessing.ipynb | jinchuika/umg-ai |
ObjetivoAplicaremos pre-procesamiento de datos para realizar una predicción en la columna `MonthlyCharges`, intentando predecir los pagos mensuales de un cliente. Para ello, seleccionamos las columnas que puedan servir como características (`X`) y nuestra variable objetivo (`y`). | # Elegir las columnas a utilizar
columnas_importantes = [
'gender',
'Partner',
'Dependents',
'SeniorCitizen',
'PhoneService',
'MultipleLines',
'InternetService',
'MonthlyCharges'
]
# Nuevo DataFrame con las columnas seleccionadas
df_limpio = df[columnas_importantes]
# Revisamos una muestra de los datos de nuestro nuevo DataFrame
df_limpio.head() | _____no_output_____ | MIT | clase4/4-1_preprocessing.ipynb | jinchuika/umg-ai |
Como podemos observar, la mayoría de columnas con datos categóricos se encuentran en formato de texto. Si deseamos utilizar esos datos en un algoritmo de inteligencia artificial, necesitamos convertirlos en representaciones numéricas. Para ello, realizaremos una tarea de pre-procesamiento utilizando un codificador de tipo `LabelEncoder`. | # Crear el encoder para la columna gender
gender_encoder = LabelEncoder()
# "Aprender" los datos para el encoder.
# Este proceso reconoce las diferentes categorías en los datos.
# Para cada categoría, asignará un número iniciando desde 0
gender_encoder.fit(df_limpio['gender'])
# Podemos transformar los datos originales en sus representaciones numéricas
gender_encoder.fit_transform(df_limpio['gender'])
# Si quisiéramos transofrmar los datos de forma inversa, es decir,
# obtener los datos originales a partir de sus representaciones,
# podemos utilizar el método invevrse_transform
gender_encoder.inverse_transform([0, 1, 1, 1, 0])
# Podemos visualizar las clases encontradas por el encoder
gender_encoder.classes_ | _____no_output_____ | MIT | clase4/4-1_preprocessing.ipynb | jinchuika/umg-ai |
Una forma más eficienteYa que necesitamos un codificador para cada columna, crearemos una estructura de datos que permita almacenar un codificador distinto para cada columna. | # Empezamos revisando la de columnas del DataFrame
for columna in df_limpio.columns:
print(columna) | gender
Partner
Dependents
SeniorCitizen
PhoneService
MultipleLines
InternetService
MonthlyCharges
| MIT | clase4/4-1_preprocessing.ipynb | jinchuika/umg-ai |
Ya que no todas las columnas necesitan ser codificadas (`SeniorCitizen` ya se encuentra codificada y `MonthlyCharges` no es un dato de tipo categórico), incluiremos únicamente las columnas con datos categóricos que necesitan ser codificados. | # Creamos un nuevo DataFrame que tendrá los datos convertidos
df_final = pd.DataFrame()
# En un diccionario, almacenamos un codificador por cada columna
encoders = {
'gender': LabelEncoder(),
'Partner': LabelEncoder(),
'Dependents': LabelEncoder(),
'PhoneService': LabelEncoder(),
'MultipleLines': LabelEncoder(),
'InternetService': LabelEncoder(),
}
# Codificar cada columna y agregarla al nuevo DataFrame
for columna, encoder in encoders.items():
encoder.fit(df_limpio[columna])
df_final[columna] = encoder.fit_transform(df_limpio[columna])
df_final.head()
# Revisión de todas las clases obtenidas por los codificadores
for column, encoder in encoders.items():
print(column, encoder.classes_)
print('=======')
# Agregamos las columnas que no transformamos al DataFrame final
df_final['SeniorCitizen'] = df_limpio['SeniorCitizen']
df_final['MonthlyCharges'] = df_limpio['MonthlyCharges']
df_final.head()
# Exportar los datos codificados a un nuevo archivo CSV
df_final.to_csv('datos_limpios.csv', index=None) | _____no_output_____ | MIT | clase4/4-1_preprocessing.ipynb | jinchuika/umg-ai |
*Data Science Unit 4 Sprint 3 Assignment 2* Convolutional Neural Networks (CNNs) Assignment- Part 1: Pre-Trained Model- Part 2: Custom CNN Model- Part 3: CNN with Data AugmentationYou will apply three different CNN models to a binary image classification model using Keras. Classify images of Mountains (`./data/mountain/*`) and images of forests (`./data/forest/*`). Treat mountains as the postive class (1) and the forest images as the negative (zero). |Mountain (+)|Forest (-)||---|---||||The problem is realively difficult given that the sample is tiny: there are about 350 observations per class. This sample size might be something that you can expect with prototyping an image classification problem/solution at work. Get accustomed to evaluating several differnet possible models. Pre - Trained ModelLoad a pretrained network from Keras, [ResNet50](https://tfhub.dev/google/imagenet/resnet_v1_50/classification/1) - a 50 layer deep network trained to recognize [1000 objects](https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt). Starting usage:```pythonimport numpy as npfrom tensorflow.keras.applications.resnet50 import ResNet50from tensorflow.keras.preprocessing import imagefrom tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictionsfrom tensorflow.keras.layers import Dense, GlobalAveragePooling2D()from tensorflow.keras.models import Model This is the functional APIresnet = ResNet50(weights='imagenet', include_top=False)```The `include_top` parameter in `ResNet50` will remove the full connected layers from the ResNet model. The next step is to turn off the training of the ResNet layers. We want to use the learned parameters without updating them in future training passes. ```pythonfor layer in resnet.layers: layer.trainable = False```Using the Keras functional API, we will need to additional additional full connected layers to our model. We we removed the top layers, we removed all preivous fully connected layers. In other words, we kept only the feature processing portions of our network. You can expert with additional layers beyond what's listed here. The `GlobalAveragePooling2D` layer functions as a really fancy flatten function by taking the average of each of the last convolutional layer outputs (which is two dimensional still). ```pythonx = res.outputx = GlobalAveragePooling2D()(x) This layer is a really fancy flattenx = Dense(1024, activation='relu')(x)predictions = Dense(1, activation='sigmoid')(x)model = Model(res.input, predictions)```Your assignment is to apply the transfer learning above to classify images of Mountains (`./data/mountain/*`) and images of forests (`./data/forest/*`). Treat mountains as the postive class (1) and the forest images as the negative (zero). Steps to complete assignment: 1. Load in Image Data into numpy arrays (`X`) 2. Create a `y` for the labels3. Train your model with pretrained layers from resnet4. Report your model's accuracy Load in DataCheck out out [`skimage`](https://scikit-image.org/) for useful functions related to processing the images. In particular checkout the documentation for `skimage.io.imread_collection` and `skimage.transform.resize`. | import numpy as np
import os
from skimage import color, io
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.models import Model
resnet = ResNet50(weights='imagenet', include_top=False)
forests = io.imread_collection('./data/forest/*')
mountains = io.imread_collection('./data/mountain/*')
len(forests), len(mountains)
forest_labels = np.zeros(len(forests))
mountain_labels = np.ones(len(mountains))
labels = np.concatenate((forest_labels, mountain_labels), axis=0)
pics = np.concatenate((forests, mountains), axis=0)
labels_train = labels[:500]
labels_test = labels[500:]
pics_train = pics[:500]
pics_test = pics[500:]
len(labels_train), len(labels_test) | _____no_output_____ | MIT | module2-convolutional-neural-networks/LS_DS_432_Convolution_Neural_Networks_Assignment.ipynb | j-m-d-h/DS-Unit-4-Sprint-3-Deep-Learning |
Instatiate Model | for layer in resnet.layers:
layer.trainable = False
x = resnet.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(1, activation='sigmoid')(x)
model = Model(resnet.input, predictions)
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) | _____no_output_____ | MIT | module2-convolutional-neural-networks/LS_DS_432_Convolution_Neural_Networks_Assignment.ipynb | j-m-d-h/DS-Unit-4-Sprint-3-Deep-Learning |
Fit Model | model.fit(pics_train, labels_train, validation_data=(pics_test, labels_test), epochs=4)
model.predict(pics[206]) | _____no_output_____ | MIT | module2-convolutional-neural-networks/LS_DS_432_Convolution_Neural_Networks_Assignment.ipynb | j-m-d-h/DS-Unit-4-Sprint-3-Deep-Learning |
Custom CNN ModelIn this step, write and train your own convolutional neural network using Keras. You can use any architecture that suits you as long as it has at least one convolutional and one pooling layer at the beginning of the network - you can add more if you want. | Train on 561 samples, validate on 141 samples
Epoch 1/5
561/561 [==============================] - 18s 32ms/sample - loss: 0.2667 - accuracy: 0.9073 - val_loss: 0.1186 - val_accuracy: 0.9858
Epoch 2/5
561/561 [==============================] - 18s 32ms/sample - loss: 0.2046 - accuracy: 0.9073 - val_loss: 0.3342 - val_accuracy: 0.8511
Epoch 3/5
561/561 [==============================] - 18s 32ms/sample - loss: 0.1778 - accuracy: 0.9287 - val_loss: 0.2746 - val_accuracy: 0.8723
Epoch 4/5
561/561 [==============================] - 18s 32ms/sample - loss: 0.1681 - accuracy: 0.9323 - val_loss: 0.8487 - val_accuracy: 0.5957
Epoch 5/5
561/561 [==============================] - 18s 32ms/sample - loss: 0.1606 - accuracy: 0.9394 - val_loss: 0.3903 - val_accuracy: 0.8582
| MIT | module2-convolutional-neural-networks/LS_DS_432_Convolution_Neural_Networks_Assignment.ipynb | j-m-d-h/DS-Unit-4-Sprint-3-Deep-Learning |
|
Custom CNN Model with Image Manipulations *This a stretch goal, and it's relatively difficult*To simulate an increase in a sample of image, you can apply image manipulation techniques: cropping, rotation, stretching, etc. Luckily Keras has some handy functions for us to apply these techniques to our mountain and forest example. Check out these resources to help you get started: 1. [Keras `ImageGenerator` Class](https://keras.io/preprocessing/image/imagedatagenerator-class)2. [Building a powerful image classifier with very little data](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html) | # State Code for Image Manipulation Here | _____no_output_____ | MIT | module2-convolutional-neural-networks/LS_DS_432_Convolution_Neural_Networks_Assignment.ipynb | j-m-d-h/DS-Unit-4-Sprint-3-Deep-Learning |
Set Up | # imports and juppyter environment settings
%run -i settings.py
# setup environment variables
%run -i setup.py
# To auto-reload modules in jupyter notebook (so that changes in files *.py doesn't require manual reloading):
# for auto-reloading external modules
# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython
%reload_ext autoreload
%autoreload 2
# you can now enable 2x images by just adding the line:
# see: https://gist.github.com/minrk/3301035
%config InlineBackend.figure_format = 'png'
file = f'{BASE_DIR}/data/circuit_court_2009_2019.csv.gz'
df = pd.read_csv(file, parse_dates =['offense_date'])
df.head(5) | _____no_output_____ | MIT | va_circuit_court_eda.ipynb | mh4ey/va-case-expungement |
Summary Statistics | # In this example, the data frame is described and [‘object’] is passed to include parameter
# to see description of object series. [.20, .40, .60, .80] is passed to percentile parameter
# to view the respective percentile of Numeric series.
# see: https://www.geeksforgeeks.org/python-pandas-dataframe-describe-method/
perc = [0.20, .40, .60, 0.80]
include = ['object', 'float', 'int']
df.describe(percentiles= perc, include=include).T | _____no_output_____ | MIT | va_circuit_court_eda.ipynb | mh4ey/va-case-expungement |
Data Cleaning | # number of na values in each column
df.isna().sum() | _____no_output_____ | MIT | va_circuit_court_eda.ipynb | mh4ey/va-case-expungement |
For our study, 'personId', 'offense_date', 'final_disposition', 'fips', 'race', 'gender', 'charge', 'fips_area' are mandatory columns.So removing all records where these field values are missing will be removed | df.dropna(axis=0, subset=['final_disposition'], inplace=True)
df.dropna(axis=0, subset=['race'], inplace=True)
df.isna().sum()
# for the moment we consider case_class as important field, so we will impute missing value 'unknown'
df['ammended_charge'].fillna('unknown', inplace=True)
df.isna().sum()
duplicate_records = pd.DataFrame(df.duplicated(), columns=['isduplicate'])
duplicate_records = duplicate_records.reset_index()
duplicate_records.columns = [str(column) for column in duplicate_records.columns]
duplicate_records.set_index('index')
duplicate_records.groupby('isduplicate').count() | _____no_output_____ | MIT | va_circuit_court_eda.ipynb | mh4ey/va-case-expungement |
Our dataset has 974935 duplicate records, so we purge these records from our dataset | #drop duplicate records
df = df.drop_duplicates(['person_id', 'offense_date', 'final_disposition', 'fips', 'race', 'gender', 'charge', 'ammended_charge'])
len(df) | _____no_output_____ | MIT | va_circuit_court_eda.ipynb | mh4ey/va-case-expungement |
df * We can see that hearing_date, fips, gender, charge_type and person_id do not have any missing data, however we need to address the missing data for other columns. | df.isna().sum() | _____no_output_____ | MIT | va_circuit_court_eda.ipynb | mh4ey/va-case-expungement |
Per our domain expert if ammended_charge is reduced to **'DWI'** then the case is a candidate for expungement. | dwi_ammended_charge_df = df [df.ammended_charge.str.contains('DWI') == True]
len(dwi_ammended_charge_df) | _____no_output_____ | MIT | va_circuit_court_eda.ipynb | mh4ey/va-case-expungement |
Our aim is to derive the cadidate cases for expungement based on hearing_results, let us examine the uniuqe values for hearing_result | # top 15 hearing results
hearing_result_counts = df['final_disposition'].value_counts()
subset = hearing_result_counts[:15]
sns.barplot(y=subset.index, x=subset.values)
# to 15 hearing results by gender
df_result_bygendger = df.groupby(['final_disposition', 'gender'])\
.size()\
.unstack()\
.fillna(0)\
.sort_values(['Female', 'Male'], ascending=False)
df_stacked = df_result_bygendger.head(15).stack()
df_stacked.name = 'total'
df_stacked= df_stacked.reset_index()
sns.barplot(x='total', y='final_disposition', hue='gender', data= df_stacked) | _____no_output_____ | MIT | va_circuit_court_eda.ipynb | mh4ey/va-case-expungement |
Preprocessing Data Adding fips_area name column | #load fips code table
fips_file = 'reference-data/va-fips-codes.csv'
fips_df = pd.read_csv(fips_file)
fips_df = fips_df[['CountyFIPSCode', 'GUName']]
fips_df
#add fips_GUName
df = pd.merge(df,fips_df,left_on='fips', right_on='CountyFIPSCode', how='left')\
.drop(columns=['CountyFIPSCode'], axis=1)\
.rename(columns={'GUName': 'fips_area'})
df | _____no_output_____ | MIT | va_circuit_court_eda.ipynb | mh4ey/va-case-expungement |
Identifying the candidates for case expungement Assumptions maded based on domain expert input:1. If the final_disposition is any the following values 'Dismissed','Noile Prosequi','Not Guilty', 'Withdrawn', 'Not Found', 'No Indictment Presented', 'No Longer Under Advisement', 'Not True Bill' then the case is candidate for expungement.2. **TODO:** If the charges are ammended to DWI then also it can be a candidate for expungement if the person does not have any prior felony charges | cand_list =['Dismissed','Noile Prosequi','Not Guilty', 'Withdrawn', 'Not Found', 'No Indictment Presented', 'No Longer Under Advisement', 'Not True Bill']
df['candidate'] = [1 if x in cand_list else 0 for x in df['final_disposition']]
df
df.groupby(['candidate']).count().head()
df.groupby(['candidate','race','gender']).count()
df.to_csv(
PROCESSED_PATH + "district_court_2009_2019_cleansed.csv.gz",
index=False,
compression="gzip",
header=True,
quotechar='"',
doublequote=True,
line_terminator="\n",
)
delete_file(PROCESSED_PATH + "district_court_2009_2019.csv.gz")
#!jupyter nbconvert va_circuit_court_eda.ipynb --to pdf | _____no_output_____ | MIT | va_circuit_court_eda.ipynb | mh4ey/va-case-expungement |
Implementing ResNet in PyTorchToday we are going to implement the famous ResNet from Kaiming He et al. (Microsoft Research). It won the 1st place on the ILSVRC 2015 classification task.The original paper can be read from [here ](https://arxiv.org/abs/1512.03385) and it is very easy to follow, additional material can be found in this [quora answer](https://www.quora.com/)*Deeper neural networks are more difficult to train.* Why? One big problem of deeper network is the vanishing gradient. Basically, the model is not able to learn anymore.To solve this problem, the Authors proposed to use a reference to the previous layer to compute the output at a given layer. In ResNet, the output form the previous layer, called **residual**, is added to the output of the current layer. The following picture visualizes this operationWe are going to make our implementation **as scalable as possible** using one think think unknown to mostly of the data scientiest: **object orienting programming** Basic BlockOkay, the first thing is to think about what we need. Well, first of all we need a convolution layer and since PyTorch does not have the 'auto' padding in Conv2d, so we have to code ourself! | class Conv2dAuto(nn.Conv2d):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.padding = (self.kernel_size[0] // 2, self.kernel_size[1] // 2) # dynamic add padding based on the kernel_size
conv3x3 = partial(Conv2dAuto, kernel_size=3, bias=False)
conv = conv3x3(in_channels=32, out_channels=64)
print(conv)
del conv | Conv2dAuto(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
| MIT | ResNet.ipynb | harshithbelagur/ResNet |
Residual BlockTo make clean code is mandatory to think about the main building block of each application, or of the network in our case. The residual block takes an input with `in_channels`, applies some blocks of convolutional layers to reduce it to `out_channels` and sum it up to the original input. If their sizes mismatch, then the input goes into an `identity`. We can abstract this process and create a interface that can be extedend. | class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.in_channels, self.out_channels = in_channels, out_channels
self.blocks = nn.Identity()
self.shortcut = nn.Identity()
def forward(self, x):
residual = x
if self.should_apply_shortcut: residual = self.shortcut(x)
x = self.blocks(x)
x += residual
return x
@property
def should_apply_shortcut(self):
return self.in_channels != self.out_channels
ResidualBlock(32, 64) | _____no_output_____ | MIT | ResNet.ipynb | harshithbelagur/ResNet |
Let's test it with a dummy vector with one one, we should get a vector with two | dummy = torch.ones((1, 1, 1, 1))
block = ResidualBlock(1, 64)
block(dummy) | _____no_output_____ | MIT | ResNet.ipynb | harshithbelagur/ResNet |
In ResNet each block has a expansion parameter in order to increase the `out_channels`. Also, the identity is defined as a Convolution followed by an Activation layer, this is referred as `shortcut`. Then, we can just extend `ResidualBlock` and defined the `shortcut` function. | from collections import OrderedDict
class ResNetResidualBlock(ResidualBlock):
def __init__(self, in_channels, out_channels, expansion=1, downsampling=1, conv=conv3x3, *args, **kwargs):
super().__init__(in_channels, out_channels)
self.expansion, self.downsampling, self.conv = expansion, downsampling, conv
self.shortcut = nn.Sequential(OrderedDict(
{
'conv' : nn.Conv2d(self.in_channels, self.expanded_channels, kernel_size=1,
stride=self.downsampling, bias=False),
'bn' : nn.BatchNorm2d(self.expanded_channels)
})) if self.should_apply_shortcut else None
@property
def expanded_channels(self):
return self.out_channels * self.expansion
@property
def should_apply_shortcut(self):
return self.in_channels != self.expanded_channels
ResNetResidualBlock(32, 64) | _____no_output_____ | MIT | ResNet.ipynb | harshithbelagur/ResNet |
Basic BlockA basic ResNet block is composed by two layers of `3x3` convs/batchnorm/relu. In the picture, the lines represnet the residual operation. The dotted line means that the shortcut was applied to match the input and the output dimension. Let's first create an handy function to stack one conv and batchnorm layer. Using `OrderedDict` to properly name each sublayer. | from collections import OrderedDict
def conv_bn(in_channels, out_channels, conv, *args, **kwargs):
return nn.Sequential(OrderedDict({'conv': conv(in_channels, out_channels, *args, **kwargs),
'bn': nn.BatchNorm2d(out_channels) }))
conv_bn(3, 3, nn.Conv2d, kernel_size=3)
class ResNetBasicBlock(ResNetResidualBlock):
expansion = 1
def __init__(self, in_channels, out_channels, activation=nn.ReLU, *args, **kwargs):
super().__init__(in_channels, out_channels, *args, **kwargs)
self.blocks = nn.Sequential(
conv_bn(self.in_channels, self.out_channels, conv=self.conv, bias=False, stride=self.downsampling),
activation(),
conv_bn(self.out_channels, self.expanded_channels, conv=self.conv, bias=False),
)
dummy = torch.ones((1, 32, 224, 224))
block = ResNetBasicBlock(32, 64)
block(dummy).shape
print(block)
'''The shortcut is the residual (the solution for vanishing gradient, skipping the 2 layers
and directly contributing to the output)''' | ResNetBasicBlock(
(blocks): Sequential(
(0): Sequential(
(conv): Conv2dAuto(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): ReLU()
(2): Sequential(
(conv): Conv2dAuto(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(shortcut): Sequential(
(conv): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
| MIT | ResNet.ipynb | harshithbelagur/ResNet |
BottleNeckTo increase the network deepths but to decrese the number of parameters, the Authors defined a BottleNeck block that "The three layers are 1x1, 3x3, and 1x1 convolutions, where the 1×1 layers are responsible for reducing and then increasing (restoring) dimensions, leaving the 3×3 layer a bottleneck with smaller input/output dimensions." We can extend the `ResNetResidualBlock` and create these blocks. | class ResNetBottleNeckBlock(ResNetResidualBlock):
expansion = 4
def __init__(self, in_channels, out_channels, activation=nn.ReLU, *args, **kwargs):
super().__init__(in_channels, out_channels, expansion=4, *args, **kwargs)
self.blocks = nn.Sequential(
conv_bn(self.in_channels, self.out_channels, self.conv, kernel_size=1),
activation(),
conv_bn(self.out_channels, self.out_channels, self.conv, kernel_size=3, stride=self.downsampling),
activation(),
conv_bn(self.out_channels, self.expanded_channels, self.conv, kernel_size=1),
)
dummy = torch.ones((1, 32, 10, 10))
block = ResNetBottleNeckBlock(32, 64)
block(dummy).shape
print(block) | ResNetBottleNeckBlock(
(blocks): Sequential(
(0): Sequential(
(conv): Conv2dAuto(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): ReLU()
(2): Sequential(
(conv): Conv2dAuto(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(3): ReLU()
(4): Sequential(
(conv): Conv2dAuto(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(shortcut): Sequential(
(conv): Conv2d(32, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
| MIT | ResNet.ipynb | harshithbelagur/ResNet |
LayerA ResNet's layer is composed by blocks stacked one after the other. We can easily defined it by just stuck `n` blocks one after the other, just remember that the first convolution block has a stide of two since "We perform downsampling directly by convolutional layers that have a stride of 2". | class ResNetLayer(nn.Module):
def __init__(self, in_channels, out_channels, block=ResNetBasicBlock, n=1, *args, **kwargs):
super().__init__()
# 'We perform downsampling directly by convolutional layers that have a stride of 2.'
downsampling = 2 if in_channels != out_channels else 1
self.blocks = nn.Sequential(
block(in_channels , out_channels, *args, **kwargs, downsampling=downsampling),
*[block(out_channels * block.expansion,
out_channels, downsampling=1, *args, **kwargs) for _ in range(n - 1)]
)
def forward(self, x):
x = self.blocks(x)
return x
dummy = torch.ones((1, 32, 48, 48))
layer = ResNetLayer(64, 128, block=ResNetBasicBlock, n=3)
# layer(dummy).shape
layer | _____no_output_____ | MIT | ResNet.ipynb | harshithbelagur/ResNet |
EncoderSimilarly, the encoder is composed by multiple layer at increasing features size. | class ResNetEncoder(nn.Module):
"""
ResNet encoder composed by increasing different layers with increasing features.
"""
def __init__(self, in_channels=3, blocks_sizes=[64, 128, 256, 512], deepths=[2,2,2,2],
activation=nn.ReLU, block=ResNetBasicBlock, *args,**kwargs):
super().__init__()
self.blocks_sizes = blocks_sizes
self.gate = nn.Sequential(
nn.Conv2d(in_channels, self.blocks_sizes[0], kernel_size=7, stride=2, padding=3, bias=False),
nn.BatchNorm2d(self.blocks_sizes[0]),
activation(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
)
self.in_out_block_sizes = list(zip(blocks_sizes, blocks_sizes[1:]))
self.blocks = nn.ModuleList([
ResNetLayer(blocks_sizes[0], blocks_sizes[0], n=deepths[0], activation=activation,
block=block, *args, **kwargs),
*[ResNetLayer(in_channels * block.expansion,
out_channels, n=n, activation=activation,
block=block, *args, **kwargs)
for (in_channels, out_channels), n in zip(self.in_out_block_sizes, deepths[1:])]
])
def forward(self, x):
x = self.gate(x)
for block in self.blocks:
x = block(x)
return x | _____no_output_____ | MIT | ResNet.ipynb | harshithbelagur/ResNet |
DecoderThe decoder is the last piece we need to create the full network. It is a fully connected layer that maps the features learned by the network to their respective classes. Easily, we can defined it as: | class ResnetDecoder(nn.Module):
"""
This class represents the tail of ResNet. It performs a global pooling and maps the output to the
correct class by using a fully connected layer.
"""
def __init__(self, in_features, n_classes):
super().__init__()
self.avg = nn.AdaptiveAvgPool2d((1, 1))
self.decoder = nn.Linear(in_features, n_classes)
def forward(self, x):
x = self.avg(x)
x = x.view(x.size(0), -1)
x = self.decoder(x)
return x
| _____no_output_____ | MIT | ResNet.ipynb | harshithbelagur/ResNet |
ResNetFinal, we can put all the pieces together and create the final model. | class ResNet(nn.Module):
def __init__(self, in_channels, n_classes, *args, **kwargs):
super().__init__()
self.encoder = ResNetEncoder(in_channels, *args, **kwargs)
self.decoder = ResnetDecoder(self.encoder.blocks[-1].blocks[-1].expanded_channels, n_classes)
def forward(self, x):
x = self.encoder(x)
x = self.decoder(x)
return x | _____no_output_____ | MIT | ResNet.ipynb | harshithbelagur/ResNet |
We can now defined the five models proposed by the Authors, `resnet18,34,50,101,152` | def resnet18(in_channels, n_classes):
return ResNet(in_channels, n_classes, block=ResNetBasicBlock, deepths=[2, 2, 2, 2])
def resnet34(in_channels, n_classes):
return ResNet(in_channels, n_classes, block=ResNetBasicBlock, deepths=[3, 4, 6, 3])
def resnet50(in_channels, n_classes):
return ResNet(in_channels, n_classes, block=ResNetBottleNeckBlock, deepths=[3, 4, 6, 3])
def resnet101(in_channels, n_classes):
return ResNet(in_channels, n_classes, block=ResNetBottleNeckBlock, deepths=[3, 4, 23, 3])
def resnet152(in_channels, n_classes):
return ResNet(in_channels, n_classes, block=ResNetBottleNeckBlock, deepths=[3, 8, 36, 3])
from torchsummary import summary
model = resnet101(3, 1000)
summary(model.cuda(), (3, 224, 224))
import torchvision.models as models
# resnet101(False)
summary(models.resnet101(False).cuda(), (3, 224, 224)) | ----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 112, 112] 9,408
BatchNorm2d-2 [-1, 64, 112, 112] 128
ReLU-3 [-1, 64, 112, 112] 0
MaxPool2d-4 [-1, 64, 56, 56] 0
Conv2d-5 [-1, 64, 56, 56] 4,096
BatchNorm2d-6 [-1, 64, 56, 56] 128
ReLU-7 [-1, 64, 56, 56] 0
Conv2d-8 [-1, 64, 56, 56] 36,864
BatchNorm2d-9 [-1, 64, 56, 56] 128
ReLU-10 [-1, 64, 56, 56] 0
Conv2d-11 [-1, 256, 56, 56] 16,384
BatchNorm2d-12 [-1, 256, 56, 56] 512
Conv2d-13 [-1, 256, 56, 56] 16,384
BatchNorm2d-14 [-1, 256, 56, 56] 512
ReLU-15 [-1, 256, 56, 56] 0
Bottleneck-16 [-1, 256, 56, 56] 0
Conv2d-17 [-1, 64, 56, 56] 16,384
BatchNorm2d-18 [-1, 64, 56, 56] 128
ReLU-19 [-1, 64, 56, 56] 0
Conv2d-20 [-1, 64, 56, 56] 36,864
BatchNorm2d-21 [-1, 64, 56, 56] 128
ReLU-22 [-1, 64, 56, 56] 0
Conv2d-23 [-1, 256, 56, 56] 16,384
BatchNorm2d-24 [-1, 256, 56, 56] 512
ReLU-25 [-1, 256, 56, 56] 0
Bottleneck-26 [-1, 256, 56, 56] 0
Conv2d-27 [-1, 64, 56, 56] 16,384
BatchNorm2d-28 [-1, 64, 56, 56] 128
ReLU-29 [-1, 64, 56, 56] 0
Conv2d-30 [-1, 64, 56, 56] 36,864
BatchNorm2d-31 [-1, 64, 56, 56] 128
ReLU-32 [-1, 64, 56, 56] 0
Conv2d-33 [-1, 256, 56, 56] 16,384
BatchNorm2d-34 [-1, 256, 56, 56] 512
ReLU-35 [-1, 256, 56, 56] 0
Bottleneck-36 [-1, 256, 56, 56] 0
Conv2d-37 [-1, 128, 56, 56] 32,768
BatchNorm2d-38 [-1, 128, 56, 56] 256
ReLU-39 [-1, 128, 56, 56] 0
Conv2d-40 [-1, 128, 28, 28] 147,456
BatchNorm2d-41 [-1, 128, 28, 28] 256
ReLU-42 [-1, 128, 28, 28] 0
Conv2d-43 [-1, 512, 28, 28] 65,536
BatchNorm2d-44 [-1, 512, 28, 28] 1,024
Conv2d-45 [-1, 512, 28, 28] 131,072
BatchNorm2d-46 [-1, 512, 28, 28] 1,024
ReLU-47 [-1, 512, 28, 28] 0
Bottleneck-48 [-1, 512, 28, 28] 0
Conv2d-49 [-1, 128, 28, 28] 65,536
BatchNorm2d-50 [-1, 128, 28, 28] 256
ReLU-51 [-1, 128, 28, 28] 0
Conv2d-52 [-1, 128, 28, 28] 147,456
BatchNorm2d-53 [-1, 128, 28, 28] 256
ReLU-54 [-1, 128, 28, 28] 0
Conv2d-55 [-1, 512, 28, 28] 65,536
BatchNorm2d-56 [-1, 512, 28, 28] 1,024
ReLU-57 [-1, 512, 28, 28] 0
Bottleneck-58 [-1, 512, 28, 28] 0
Conv2d-59 [-1, 128, 28, 28] 65,536
BatchNorm2d-60 [-1, 128, 28, 28] 256
ReLU-61 [-1, 128, 28, 28] 0
Conv2d-62 [-1, 128, 28, 28] 147,456
BatchNorm2d-63 [-1, 128, 28, 28] 256
ReLU-64 [-1, 128, 28, 28] 0
Conv2d-65 [-1, 512, 28, 28] 65,536
BatchNorm2d-66 [-1, 512, 28, 28] 1,024
ReLU-67 [-1, 512, 28, 28] 0
Bottleneck-68 [-1, 512, 28, 28] 0
Conv2d-69 [-1, 128, 28, 28] 65,536
BatchNorm2d-70 [-1, 128, 28, 28] 256
ReLU-71 [-1, 128, 28, 28] 0
Conv2d-72 [-1, 128, 28, 28] 147,456
BatchNorm2d-73 [-1, 128, 28, 28] 256
ReLU-74 [-1, 128, 28, 28] 0
Conv2d-75 [-1, 512, 28, 28] 65,536
BatchNorm2d-76 [-1, 512, 28, 28] 1,024
ReLU-77 [-1, 512, 28, 28] 0
Bottleneck-78 [-1, 512, 28, 28] 0
Conv2d-79 [-1, 256, 28, 28] 131,072
BatchNorm2d-80 [-1, 256, 28, 28] 512
ReLU-81 [-1, 256, 28, 28] 0
Conv2d-82 [-1, 256, 14, 14] 589,824
BatchNorm2d-83 [-1, 256, 14, 14] 512
ReLU-84 [-1, 256, 14, 14] 0
Conv2d-85 [-1, 1024, 14, 14] 262,144
BatchNorm2d-86 [-1, 1024, 14, 14] 2,048
Conv2d-87 [-1, 1024, 14, 14] 524,288
BatchNorm2d-88 [-1, 1024, 14, 14] 2,048
ReLU-89 [-1, 1024, 14, 14] 0
Bottleneck-90 [-1, 1024, 14, 14] 0
Conv2d-91 [-1, 256, 14, 14] 262,144
BatchNorm2d-92 [-1, 256, 14, 14] 512
ReLU-93 [-1, 256, 14, 14] 0
Conv2d-94 [-1, 256, 14, 14] 589,824
BatchNorm2d-95 [-1, 256, 14, 14] 512
ReLU-96 [-1, 256, 14, 14] 0
Conv2d-97 [-1, 1024, 14, 14] 262,144
BatchNorm2d-98 [-1, 1024, 14, 14] 2,048
ReLU-99 [-1, 1024, 14, 14] 0
Bottleneck-100 [-1, 1024, 14, 14] 0
Conv2d-101 [-1, 256, 14, 14] 262,144
BatchNorm2d-102 [-1, 256, 14, 14] 512
ReLU-103 [-1, 256, 14, 14] 0
Conv2d-104 [-1, 256, 14, 14] 589,824
BatchNorm2d-105 [-1, 256, 14, 14] 512
ReLU-106 [-1, 256, 14, 14] 0
Conv2d-107 [-1, 1024, 14, 14] 262,144
BatchNorm2d-108 [-1, 1024, 14, 14] 2,048
ReLU-109 [-1, 1024, 14, 14] 0
Bottleneck-110 [-1, 1024, 14, 14] 0
Conv2d-111 [-1, 256, 14, 14] 262,144
BatchNorm2d-112 [-1, 256, 14, 14] 512
ReLU-113 [-1, 256, 14, 14] 0
Conv2d-114 [-1, 256, 14, 14] 589,824
BatchNorm2d-115 [-1, 256, 14, 14] 512
ReLU-116 [-1, 256, 14, 14] 0
Conv2d-117 [-1, 1024, 14, 14] 262,144
BatchNorm2d-118 [-1, 1024, 14, 14] 2,048
ReLU-119 [-1, 1024, 14, 14] 0
Bottleneck-120 [-1, 1024, 14, 14] 0
Conv2d-121 [-1, 256, 14, 14] 262,144
BatchNorm2d-122 [-1, 256, 14, 14] 512
ReLU-123 [-1, 256, 14, 14] 0
Conv2d-124 [-1, 256, 14, 14] 589,824
BatchNorm2d-125 [-1, 256, 14, 14] 512
ReLU-126 [-1, 256, 14, 14] 0
Conv2d-127 [-1, 1024, 14, 14] 262,144
BatchNorm2d-128 [-1, 1024, 14, 14] 2,048
ReLU-129 [-1, 1024, 14, 14] 0
Bottleneck-130 [-1, 1024, 14, 14] 0
Conv2d-131 [-1, 256, 14, 14] 262,144
BatchNorm2d-132 [-1, 256, 14, 14] 512
ReLU-133 [-1, 256, 14, 14] 0
Conv2d-134 [-1, 256, 14, 14] 589,824
BatchNorm2d-135 [-1, 256, 14, 14] 512
ReLU-136 [-1, 256, 14, 14] 0
Conv2d-137 [-1, 1024, 14, 14] 262,144
BatchNorm2d-138 [-1, 1024, 14, 14] 2,048
ReLU-139 [-1, 1024, 14, 14] 0
Bottleneck-140 [-1, 1024, 14, 14] 0
Conv2d-141 [-1, 256, 14, 14] 262,144
BatchNorm2d-142 [-1, 256, 14, 14] 512
ReLU-143 [-1, 256, 14, 14] 0
Conv2d-144 [-1, 256, 14, 14] 589,824
BatchNorm2d-145 [-1, 256, 14, 14] 512
ReLU-146 [-1, 256, 14, 14] 0
Conv2d-147 [-1, 1024, 14, 14] 262,144
BatchNorm2d-148 [-1, 1024, 14, 14] 2,048
ReLU-149 [-1, 1024, 14, 14] 0
Bottleneck-150 [-1, 1024, 14, 14] 0
Conv2d-151 [-1, 256, 14, 14] 262,144
BatchNorm2d-152 [-1, 256, 14, 14] 512
ReLU-153 [-1, 256, 14, 14] 0
Conv2d-154 [-1, 256, 14, 14] 589,824
BatchNorm2d-155 [-1, 256, 14, 14] 512
ReLU-156 [-1, 256, 14, 14] 0
Conv2d-157 [-1, 1024, 14, 14] 262,144
BatchNorm2d-158 [-1, 1024, 14, 14] 2,048
ReLU-159 [-1, 1024, 14, 14] 0
Bottleneck-160 [-1, 1024, 14, 14] 0
Conv2d-161 [-1, 256, 14, 14] 262,144
BatchNorm2d-162 [-1, 256, 14, 14] 512
ReLU-163 [-1, 256, 14, 14] 0
Conv2d-164 [-1, 256, 14, 14] 589,824
BatchNorm2d-165 [-1, 256, 14, 14] 512
ReLU-166 [-1, 256, 14, 14] 0
Conv2d-167 [-1, 1024, 14, 14] 262,144
BatchNorm2d-168 [-1, 1024, 14, 14] 2,048
ReLU-169 [-1, 1024, 14, 14] 0
Bottleneck-170 [-1, 1024, 14, 14] 0
Conv2d-171 [-1, 256, 14, 14] 262,144
BatchNorm2d-172 [-1, 256, 14, 14] 512
ReLU-173 [-1, 256, 14, 14] 0
Conv2d-174 [-1, 256, 14, 14] 589,824
BatchNorm2d-175 [-1, 256, 14, 14] 512
ReLU-176 [-1, 256, 14, 14] 0
Conv2d-177 [-1, 1024, 14, 14] 262,144
BatchNorm2d-178 [-1, 1024, 14, 14] 2,048
ReLU-179 [-1, 1024, 14, 14] 0
Bottleneck-180 [-1, 1024, 14, 14] 0
Conv2d-181 [-1, 256, 14, 14] 262,144
BatchNorm2d-182 [-1, 256, 14, 14] 512
ReLU-183 [-1, 256, 14, 14] 0
Conv2d-184 [-1, 256, 14, 14] 589,824
BatchNorm2d-185 [-1, 256, 14, 14] 512
ReLU-186 [-1, 256, 14, 14] 0
Conv2d-187 [-1, 1024, 14, 14] 262,144
BatchNorm2d-188 [-1, 1024, 14, 14] 2,048
ReLU-189 [-1, 1024, 14, 14] 0
Bottleneck-190 [-1, 1024, 14, 14] 0
Conv2d-191 [-1, 256, 14, 14] 262,144
BatchNorm2d-192 [-1, 256, 14, 14] 512
ReLU-193 [-1, 256, 14, 14] 0
Conv2d-194 [-1, 256, 14, 14] 589,824
BatchNorm2d-195 [-1, 256, 14, 14] 512
ReLU-196 [-1, 256, 14, 14] 0
Conv2d-197 [-1, 1024, 14, 14] 262,144
BatchNorm2d-198 [-1, 1024, 14, 14] 2,048
ReLU-199 [-1, 1024, 14, 14] 0
Bottleneck-200 [-1, 1024, 14, 14] 0
Conv2d-201 [-1, 256, 14, 14] 262,144
BatchNorm2d-202 [-1, 256, 14, 14] 512
ReLU-203 [-1, 256, 14, 14] 0
Conv2d-204 [-1, 256, 14, 14] 589,824
BatchNorm2d-205 [-1, 256, 14, 14] 512
ReLU-206 [-1, 256, 14, 14] 0
Conv2d-207 [-1, 1024, 14, 14] 262,144
BatchNorm2d-208 [-1, 1024, 14, 14] 2,048
ReLU-209 [-1, 1024, 14, 14] 0
Bottleneck-210 [-1, 1024, 14, 14] 0
Conv2d-211 [-1, 256, 14, 14] 262,144
BatchNorm2d-212 [-1, 256, 14, 14] 512
ReLU-213 [-1, 256, 14, 14] 0
Conv2d-214 [-1, 256, 14, 14] 589,824
BatchNorm2d-215 [-1, 256, 14, 14] 512
ReLU-216 [-1, 256, 14, 14] 0
Conv2d-217 [-1, 1024, 14, 14] 262,144
BatchNorm2d-218 [-1, 1024, 14, 14] 2,048
ReLU-219 [-1, 1024, 14, 14] 0
Bottleneck-220 [-1, 1024, 14, 14] 0
Conv2d-221 [-1, 256, 14, 14] 262,144
BatchNorm2d-222 [-1, 256, 14, 14] 512
ReLU-223 [-1, 256, 14, 14] 0
Conv2d-224 [-1, 256, 14, 14] 589,824
BatchNorm2d-225 [-1, 256, 14, 14] 512
ReLU-226 [-1, 256, 14, 14] 0
Conv2d-227 [-1, 1024, 14, 14] 262,144
BatchNorm2d-228 [-1, 1024, 14, 14] 2,048
ReLU-229 [-1, 1024, 14, 14] 0
Bottleneck-230 [-1, 1024, 14, 14] 0
Conv2d-231 [-1, 256, 14, 14] 262,144
BatchNorm2d-232 [-1, 256, 14, 14] 512
ReLU-233 [-1, 256, 14, 14] 0
Conv2d-234 [-1, 256, 14, 14] 589,824
BatchNorm2d-235 [-1, 256, 14, 14] 512
ReLU-236 [-1, 256, 14, 14] 0
Conv2d-237 [-1, 1024, 14, 14] 262,144
BatchNorm2d-238 [-1, 1024, 14, 14] 2,048
ReLU-239 [-1, 1024, 14, 14] 0
Bottleneck-240 [-1, 1024, 14, 14] 0
Conv2d-241 [-1, 256, 14, 14] 262,144
BatchNorm2d-242 [-1, 256, 14, 14] 512
ReLU-243 [-1, 256, 14, 14] 0
Conv2d-244 [-1, 256, 14, 14] 589,824
BatchNorm2d-245 [-1, 256, 14, 14] 512
ReLU-246 [-1, 256, 14, 14] 0
Conv2d-247 [-1, 1024, 14, 14] 262,144
BatchNorm2d-248 [-1, 1024, 14, 14] 2,048
ReLU-249 [-1, 1024, 14, 14] 0
Bottleneck-250 [-1, 1024, 14, 14] 0
Conv2d-251 [-1, 256, 14, 14] 262,144
BatchNorm2d-252 [-1, 256, 14, 14] 512
| MIT | ResNet.ipynb | harshithbelagur/ResNet |
Our modeling approach- We fully characterize the existence of a 1-round protocol by a Mixed Integer Linear Program. | from sage.all import *
import itertools
import random
from sage.sat.solvers.satsolver import SAT
from sage.sat.solvers.cryptominisat import CryptoMiniSat
from sage.misc.temporary_file import atomic_write
import copy
import time
solver = SAT(solver="LP")
solver.add_clause((-1,2))
solver.add_clause((1,3))
solution = solver()
print ' solution =',solution
lst = ['play', 'ground']
print 'ground' in lst
m = matrix(QQ, [[1,2],[4,2],[11,3]])
print 'm = ',m
x = []
for v in m:
x.append(v)
x = matrix(QQ, x)
print 'x = ',x
pr = Permutations(20).random_element()
print pr, pr[4], pr[7]
v = vector(QQ, [2,3,4])
print 'len = ',len(v)
min(5, 44)
l = [1,2,3,4,5]
print l [2:4]
# TODO: check correctness etc
def get_at(tup, a, l):
"""Given 2*l elements flattened into a tuple, pick 1 out of each pair according to array of bits."""
assert len(tup) == 2*l
assert len(a) == l
for bit in a:
assert bit in (0, 1)
# tup structure is: $(t_{0,0}, t_{0,1}, t_{1,0}, t_{1,1}, t_{2,0}, t_{2,1})$
return tuple(tup[2*i + a[i]] for i in range(l))
assert get_at((3, 13, 85, 95), (0, 1), 2) == (3, 95)
def tuples_fixed_proj(l, proj_ind, proj_val):
# h = randint(1,4000)
# if (h == 5):
# print '==================== DEBUGGING ========== a,g = ', proj_ind, proj_val
tuples = list()
x = [0 for i in range(2*l)]
for i in range(l):
x[i*2 + proj_ind[i]] = proj_val[i]
for g in itertools.product(range(2), repeat = l):
for i in range(l):
x[i*2 + 1 - proj_ind[i]] = g[i]
# if (h == 5):
# print 'modified x to ', x
tuples.append(tuple(x[:]))
return tuples
# Hack from correctness and server security (!)
# strength = 0 is consistent with standard LP. strength = 1 attempts to better sort things out
def add_no_id_a(solver, p0, q1, strength = 0):
if strength == -1:
return
for i in range(3):
for a in itertools.product(range(2), repeat = l):
for j in range(3):
if (j > i):
# this is relaxed
if strength == 0:
solver.add_constraint(sum(q1[(i, a, zeros, b)] for b in range(2)) + sum(q1[(j, a, zeros, b)]
for b in range(2)) <= 1)
elif strength == 1:
for b1 in range(2):
for b2 in range(2):
solver.add_constraint(p0[(i, a, zeros, b1)] + p0[(i, a, zeros, b2)] <= 1)
# TODO: change the name to something more informative.
# Hack #2 from correctness: For every i,a,b with p^i_a > 0, there exists g so that (i,a,g,b) > 0.
# This requires some trial and error. Otherwise, one xi at least remains without TO-input options
# This will eventually not be part of the client constraints. Currently used to `direct' the soutions.
# Another hack from correctness. For all b, there exists at least one value of t, so
# that every a reading this g will output b. Thus, the resulting sum of probabilities for
# that g is 1. There may be additional g's (the g results from some V assigned to server for each output
# value).
def get_rand_tup(len):
return tuple([sage.misc.prandom.choice(range(2)) for i in range(len)])
# i = -1 , search for an index
# i = -2 : auxiliary for relaxed mode
# [1,0,0]
# [0,1,0]
# [0,0,1]
# [0,0,0]
def safe_insert(val_l, ind_l, (i,ind), v):
to_search = []
if i >= 0:
to_search = [i]
elif i == -1:
to_search = [j for j in range(3)]
elif i == -2:
val_l[ind_l.index(ind)] = v
return
for j in to_search:
if (j,ind) in ind_l:
val_l[ind_l.index((j,ind))] = v
def find_set_server_vars(l, p0):
try:
print 'How much is server variable space limited by this solution & correctness?'
print 'List = ',p0
p1 = []
for i in range(3):
for t in itertools.product(range(2), repeat = 2*l):
p1.append((i,t))
for (y,(a,g,b)) in p0:
for j in range(3):
if ((b == 0) and (i == j)) or ((b == 1) and (i != j)):
all = tuples_fixed_proj(l, a, g)
for t in all:
try:
p1.remove((j, t))
except Exception as e:
pass
print 'number of remaining server values =',len(p1)
if (len(p1) > 0):
print 'server variables are ',p1
except: 'Uncaught exception!'
def permute_rows(m, b):
nrows = m.nrows()
perm = Permutations(nrows).random_element()
pm = [row for row in m]
pb = [v for v in b]
new_m = matrix(QQ, [pm[perm[i] - 1] for i in range(nrows)])
new_b = vector(QQ, [pb[perm[i] - 1] for i in range(nrows)])
return (new_m, new_b)
def get_full_rank_sub(m, b):
nrows = m.nrows()
new_mat = []
new_b = []
# print 'into get_full_rank_sub',m.nrows(), m.ncols()
for (i,row) in enumerate(m):
so_far = (matrix(QQ, new_mat)).transpose()
try:
so_far\row
except Exception as e:
# print 'at full rank sub Exception = ',e,i
new_mat.append(row)
new_b.append(b[i])
new_mat = matrix(QQ, new_mat)
new_b = vector(new_b)
return (new_mat, new_b)
def print_matrix(m):
if m.ncols < 37:
print m
return
per_row = 30
for row in m:
n = ceil(QQ(m.nrows())/per_row)
for i in range(n):
ran = min(per_row, m.nrows() - i * per_row)
print [row[i * per_row + j] for j in range(ran)]
print '\n'
def non_zeros(m):
return sum(sum(1 for j in row if abs(j) != 0) for row in m)
def get_inv_sub_matrix(m, b):
(new_mat, new_b) = get_full_rank_sub(m, b)
ncols = new_mat.ncols()
print 'new_mat rank / ',new_mat.rank(),'(',new_mat.nrows(),',',new_mat.ncols(),')'
new_mat_tr = new_mat.transpose()
b_dum = vector(QQ, [0 for i in range(ncols)])
(full_sub, b_dum) = permute_rows(new_mat_tr, b_dum)
(full_sub, b_dum) = get_full_rank_sub(full_sub, b_dum)
inv_sub = full_sub.inverse()
print 'x = A^{-1}*b',
# print full_sub
det_A = full_sub.determinant()
print '|A| = ', det_A
# Finding independent columns
print 'Sparsity parameters',non_zeros(full_sub),' => ',non_zeros(inv_sub)
x = inv_sub * new_b
print 'x = ',x
print 'len(x) = ',len(x)
if (full_sub.nrows() < 50 and abs(det_A) > 1) or (full_sub.ncols() < 40):
print print_matrix(inv_sub)
return x
def test_linear_solutions_sub(l, t = 3, p = 5):
b = []
constraints = []
print 'generating constraints ...'
freq = [0,0,0]
ag_set = []
a_set = []
for a in itertools.product(range(2), repeat = l):
i = sage.misc.prandom.choice(range(p))
if (i <= t):
a_set.append(a)
for g in itertools.product(range(2), repeat = l):
ag_set.append((a,g))
ag_set_len = len(ag_set)
print 'a_set = ',a_set
for t in itertools.product(range(2), repeat = 2*l):
cur_row = [0 for i in range(ag_set_len)]
b.append(1)
for a in a_set:
cur_row[ag_set.index((a, get_at(t,a,l)))] = 1
constraints.append(cur_row)
distr = [[0 for j in range(ag_set_len)] for i in range(3)]
present = [0,0,0]
for a in a_set:
y = sage.misc.prandom.choice(range(3))
present[y] = 1
for g in itertools.product(range(2), repeat = l):
distr[y][ag_set.index((a,zeros))] = 1
if sum(present[y] for y in range(3)) < 3:
print 'Unluckly choice. Aborting execution...'
for y in range(3):
constraints.append(distr[y])
b.append(1)
m_constr = matrix(QQ,constraints)
print_matrix(m_constr)
right_side = vector(QQ, b)
try:
v = m_constr\right_side
print 'solution = ',v
print 'len(solution) = ',len(v)
nz = sum(1 for x in v if abs(x) > 0)
print 'non-zeros ', nz
except Exception as e:
print 'Caught exception ',e
my_eps = QQ(1/10000)
import time
for l in range(3, 4):
for step in range(5):
t0 = time.time()
print 'finding a solution for l=',l
zeros = tuple((0 for i in range(l)))
try:
q = test_linear_solutions_sub(l, 4, 5)
finally:
print 'time =', time.time() - t0,'\n'
| finding a solution for l= 3
generating constraints ...
a_set = [(0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1), (1, 1, 0), (1, 1, 1)]
[1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0]
[1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]
[0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0]
[0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0]
[1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0]
[1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0]
[0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0]
[0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0]
[0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0]
[0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]
[0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0]
[0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0]
[0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0]
[0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0]
[0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0]
[0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0]
[1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0]
[1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0]
[0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0]
[0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0]
[1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0]
[1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1]
[0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0]
[0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1]
[0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0]
[0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0]
[0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0]
[0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0]
[0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0]
[0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1]
[0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0]
[0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1]
[0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0]
[0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]
[0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0]
[0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0]
[0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0]
[0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0]
[0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0]
[0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0]
[0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0]
[0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]
[0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0]
[0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0]
[0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0]
[0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0]
[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0]
[0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0]
[0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0]
[0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0]
[0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0]
[0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0]
[0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1]
[0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0]
[0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1]
[0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0]
[0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0]
[0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0]
[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0]
[0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0]
[0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1]
[0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0]
[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1]
[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
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Caught exception matrix equation has no solutions
time = 0.0235588550568
finding a solution for l= 3
generating constraints ...
a_set = [(0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1), (1, 1, 0), (1, 1, 1)]
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[0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0]
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[0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0]
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[0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
Caught exception matrix equation has no solutions
time = 0.0284080505371
finding a solution for l= 3
generating constraints ...
a_set = [(0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1), (1, 1, 0), (1, 1, 1)]
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[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0]
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Caught exception matrix equation has no solutions
time = 0.0441679954529
finding a solution for l= 3
generating constraints ...
a_set = [(0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1), (1, 1, 0), (1, 1, 1)]
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[1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
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Caught exception matrix equation has no solutions
time = 0.0235769748688
finding a solution for l= 3
generating constraints ...
a_set = [(0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1), (1, 1, 0), (1, 1, 1)]
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[0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0]
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[0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1]
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[0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0]
[0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1]
[0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0]
[0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1]
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[0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0]
[0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0]
[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0]
[0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0]
[0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1]
[0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0]
[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1]
[1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1]
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Caught exception matrix equation has no solutions
time = 0.0199320316315
| MIT | 3-eq-ILP-lab-mini-suf-submatrices.ipynb | anpc/perfect-reductions |
Evaluate, optimize, and fit a classifier BackgroundBefore we can begin making crop/non-crop predictions, we first need to evaluate, optimize, and fit a classifier. Because we are working with spatial data, and because the size of our training dataset is relatively small (by machine learning standards), we need to implement some lesser known methods for evaluating and optimizing our model. These include implementing spatially explicit k-fold cross-validation techniques, running nested k-fold cross validation, and fitting a model on our entire dataset. DescriptionIn this notebook, we will use the training data collected in the first notebook (`1_Extract_training_data.ipynb`) to fit and evaluate a classifier. The steps undertaken are:1. Spatially cluster our training data to visualize spatial groupings2. Calculate an unbiased performance estimate via **nested, k-fold cross-validation**. 3. Optimize the hyperparameters of the model using `GridSearchCV`4. Fit a model on _all_ the data using the parameters identified in step 35. Save the model to disk *** Getting startedTo run this analysis, run all the cells in the notebook, starting with the "Load packages" cell. Load packages | # from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
import os
import joblib
import numpy as np
import pandas as pd
from joblib import dump
from pprint import pprint
import matplotlib.pyplot as plt
from odc.io.cgroups import get_cpu_quota
from sklearn.model_selection import GridSearchCV, ShuffleSplit, KFold
from sklearn.metrics import roc_curve, auc, f1_score, balanced_accuracy_score
| _____no_output_____ | Apache-2.0 | testing/sahel_cropmask/3_Train_fit_evaluate_classifier.ipynb | digitalearthafrica/crop-mask |
Analysis Parameters* `training_data`: Name and location of the training data `.txt` file output from runnning `1_Extract_training_data.ipynb`* `coordinate_data`: Name and location of the coordinate data `.txt` file output from runnning `1_Extract_training_data.ipynb`* `Classifier`: This parameter refers to the scikit-learn classification model to use, first uncomment the classifier of interest in the `Load Packages` section and then enter the function name into this parameter `e.g. Classifier = RandomForestClassifier` * `metric` : A single string that denotes the scorer used to optimize the model. See the scoring parameter page [here](https://scikit-learn.org/stable/modules/model_evaluation.htmlscoring-parameter) for a pre-defined list of options. For binary classifications, 'F1' or 'balanced_accuracy' are good metrics.* `output_suffix`: A suffix to add to the exported model corresponding to the model iteration. | training_data = "results/training_data/sahel_training_data_20211110.txt"
Classifier = RandomForestClassifier
metric = 'balanced_accuracy'
output_suffix = '20211110' | _____no_output_____ | Apache-2.0 | testing/sahel_cropmask/3_Train_fit_evaluate_classifier.ipynb | digitalearthafrica/crop-mask |
K-Fold Cross Validation Parameters* `inner_cv_splits` : Number of cross-validation splits to do on the inner loop, e.g. `5`* `outer_cv_splits` : Number of cross-validation splits to do on the outer loop, e.g. `5`* `test_size` : This will determine what fraction of the dataset will be set aside as the testing dataset. There is a trade-off here between having a larger test set that will help us better determine the quality of our classifier, and leaving enough data to train the classifier. A good deafult is to set 10-20 % of your dataset aside for testing purposes. | inner_cv_splits = 5
outer_cv_splits = 5
test_size = 0.20
| _____no_output_____ | Apache-2.0 | testing/sahel_cropmask/3_Train_fit_evaluate_classifier.ipynb | digitalearthafrica/crop-mask |
Automatically find the number of cpus | ncpus=round(get_cpu_quota())
print('ncpus = '+str(ncpus)) | ncpus = 15
| Apache-2.0 | testing/sahel_cropmask/3_Train_fit_evaluate_classifier.ipynb | digitalearthafrica/crop-mask |
Import training and coordinate data | # load the data
model_input = np.loadtxt(training_data)
# coordinates = np.loadtxt(coordinate_data)
# load the column_names
with open(training_data, 'r') as file:
header = file.readline()
column_names = header.split()[1:]
# Extract relevant indices from training data
model_col_indices = [column_names.index(var_name) for var_name in column_names[1:]]
#convert variable names into sci-kit learn nomenclature
X = model_input[:, model_col_indices]
y = model_input[:, 0] | _____no_output_____ | Apache-2.0 | testing/sahel_cropmask/3_Train_fit_evaluate_classifier.ipynb | digitalearthafrica/crop-mask |
Evaluating the classifierNow that we're happy with the spatial clustering, we can evaluate the classifier via _nested_, K-fold cross-validation.The k-fold cross-validation procedure is used to estimate the performance of machine learning models when making predictions on data not used during training. However, when the same cross-validation procedure and dataset are used to both tune the hyperparameters and select a model, it is likely to lead to an optimistically biased evaluation of the model performance.One approach to overcoming this bias is to nest the hyperparameter optimization procedure under the model selection procedure. This is called nested cross-validation and is the preferred way to evaluate and compare tuned machine learning models. _Figure 1: Nested K-Fold Cross Validation_ ***Before evaluating the model, we need to set up some hyperparameters to test during optimization. The `param_grid` object below is set up to test various important hyperparameters for a Random Forest model. > **Note**: the parameters in the `param_grid` object depend on the classifier being used. This notebook is set up for a random forest classifier, to adjust the paramaters to suit a different classifier, look up the important parameters under the relevant [sklearn documentation](https://scikit-learn.org/stable/supervised_learning.html). | # Create the parameter grid
param_grid = {
'max_features': ['auto', 'log2', None],
'n_estimators': [150,200,250,300,350,400],
'criterion':['gini', 'entropy']
} | _____no_output_____ | Apache-2.0 | testing/sahel_cropmask/3_Train_fit_evaluate_classifier.ipynb | digitalearthafrica/crop-mask |
Now we will conduct the nested CV using the SKCV function. This will take a while to run depending on the number of inner and outer cv splits | outer_cv = KFold(n_splits=outer_cv_splits, shuffle=True,
random_state=0)
# lists to store results of CV testing
acc = []
f1 = []
roc_auc = []
i = 1
for train_index, test_index in outer_cv.split(X, y):
print(f"Working on {i}/{outer_cv_splits} outer cv split", end='\r')
model = Classifier(random_state=1)
# index training, testing, and coordinate data
X_tr, X_tt = X[train_index, :], X[test_index, :]
y_tr, y_tt = y[train_index], y[test_index]
# inner split on data within outer split
inner_cv = KFold(n_splits=inner_cv_splits,
shuffle=True,
random_state=0)
clf = GridSearchCV(
estimator=model,
param_grid=param_grid,
scoring=metric,
n_jobs=ncpus,
refit=True,
cv=inner_cv.split(X_tr, y_tr),
)
clf.fit(X_tr, y_tr)
# predict using the best model
best_model = clf.best_estimator_
pred = best_model.predict(X_tt)
# evaluate model w/ multiple metrics
# ROC AUC
probs = best_model.predict_proba(X_tt)
probs = probs[:, 1]
fpr, tpr, thresholds = roc_curve(y_tt, probs)
auc_ = auc(fpr, tpr)
roc_auc.append(auc_)
# Overall accuracy
ac = balanced_accuracy_score(y_tt, pred)
acc.append(ac)
# F1 scores
f1_ = f1_score(y_tt, pred)
f1.append(f1_)
i += 1 | Working on 5/5 outer cv split
| Apache-2.0 | testing/sahel_cropmask/3_Train_fit_evaluate_classifier.ipynb | digitalearthafrica/crop-mask |
The results of our model evaluation | print("=== Nested K-Fold Cross-Validation Scores ===")
print("Mean balanced accuracy: "+ str(round(np.mean(acc), 2)))
print("Std balanced accuracy: "+ str(round(np.std(acc), 2)))
print('\n')
print("Mean F1: "+ str(round(np.mean(f1), 2)))
print("Std F1: "+ str(round(np.std(f1), 2)))
print('\n')
print("Mean roc_auc: "+ str(round(np.mean(roc_auc), 3)))
print("Std roc_auc: "+ str(round(np.std(roc_auc), 2))) | === Nested K-Fold Cross-Validation Scores ===
Mean balanced accuracy: 0.82
Std balanced accuracy: 0.01
Mean F1: 0.77
Std F1: 0.01
Mean roc_auc: 0.916
Std roc_auc: 0.01
| Apache-2.0 | testing/sahel_cropmask/3_Train_fit_evaluate_classifier.ipynb | digitalearthafrica/crop-mask |
These scores represent a robust estimate of the accuracy of our classifier. However, because we are using only a subset of data to fit and optimize the models, it is reasonable to expect these scores are an under-estimate of the final model's accuracy. Also, the _map_ accuracy will differ from the accuracies reported here since the training data is not a perfect representation of the data in the real world (e.g. if we have purposively over-sampled from hard-to-classify regions, or if the proportions of crop to non-crop do not match the proportions in the real world). Optimize hyperparametersHyperparameter searches are a required process in machine learning. Machine learning models require certain “hyperparameters”; model parameters that can be learned from the data. Finding good values for these parameters is a “hyperparameter search” or an “hyperparameter optimization.”To optimize the parameters in our model, we use [GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) to exhaustively search through a set of parameters and determine the combination that will result in the highest accuracy based upon the accuracy metric defined. | #generate n_splits of train-test_split
rs = ShuffleSplit(n_splits=outer_cv_splits, test_size=test_size, random_state=0)
# #instatiate a gridsearchCV
clf = GridSearchCV(Classifier(),
param_grid,
scoring=metric,
verbose=1,
cv=rs.split(X, y),
n_jobs=ncpus)
clf.fit(X, y)
print("The most accurate combination of tested parameters is: ")
pprint(clf.best_params_)
print('\n')
print("The "+metric+" score using these parameters is: ")
print(round(clf.best_score_, 2)) | Fitting 5 folds for each of 36 candidates, totalling 180 fits
The most accurate combination of tested parameters is:
{'criterion': 'entropy', 'max_features': None, 'n_estimators': 250}
The balanced_accuracy score using these parameters is:
0.82
| Apache-2.0 | testing/sahel_cropmask/3_Train_fit_evaluate_classifier.ipynb | digitalearthafrica/crop-mask |
Fit a modelUsing the best parameters from our hyperparmeter optmization search, we now fit our model on all the data to give the best possible model. | #create a new model
# new_model = Classifier(**clf.best_params_, random_state=1)
new_model = Classifier(**{'criterion': 'entropy', 'max_features': None, 'n_estimators': 200}, random_state=1)
new_model.fit(X, y) | _____no_output_____ | Apache-2.0 | testing/sahel_cropmask/3_Train_fit_evaluate_classifier.ipynb | digitalearthafrica/crop-mask |
Save the modelRunning this cell will export the classifier as a binary`.joblib` file. This will allow for importing the model in the subsequent script, `4_Predict.ipynb` | model_filename = 'results/sahel_ml_model_'+output_suffix+'.joblib'
dump(new_model, model_filename) | _____no_output_____ | Apache-2.0 | testing/sahel_cropmask/3_Train_fit_evaluate_classifier.ipynb | digitalearthafrica/crop-mask |
Loading resultsResults have the format: {conf:table_of_results},where **conf** represents the experimental setting and is the tuple (0-remove_stopwords, 1-remove_punctuation, 2-termsim_metric, 3-normalization, 4-termsim_th, 5-synsets_taken, 6-wscheme, 7-sim_metric) There is another configuration for last experiments with did:Configuration format0. Removing stopwords flag : [True, False]1. Removing punctuation flag : [True, False]2. Wordnet similarity metrics : ["path", "lch", "wup", "res", "jcn", "lin"]3. Features extracted to compute similarity : ["token", "lemma", "stem", "lemmapos"]4. Features used to extract synsets : ["token", "lemma", "lemmapos"]5. Information Content used in some WordNet metrics : ["bnc_ic_2007", "bnc_ic_2000", "semcor_ic", "brown_ic"]6. Normalization flag : [True, False]7. Term-Term similarity minimum threslhold : [0.0, 0.25, 0.5, 0.75] 8. Synsets selection strategy (all-vs-all, first) : ["all", "first"]9. Features weighting scheme : ["tf", "binary"]10. Text similarity method : ["mihalcea", "softcosine", "stevenson"] **table_of_results** is a numpy 2d array with scores for each **similarity treshold** in the range **range(0, 101, 5)** sorted by the best accuracy. The **scores** (columns) are: scores = (0-threshold, 1-accuracy, 2-f_measure, 3-precision, 4-recall) | #experiments = pickle.load(open("./Results/results_20170620_complex_normalization.pickle", 'rb'))
experiments = pickle.load(open("./Results/results_20170830_normalization_4termsimTh_Rada_Stevenson_Softcosine.pickle", 'rb'))
len(experiments)
# res = (threshold, accuracy, f_measure, precision, recall)
max([(conf, res[0][0], res[0][1], res[0][2])
for conf, res in experiments.items()
#if conf[2] == "path"
]
, key=lambda x:x[2])
#[(conf, res[0][0], res[0][1]) for conf, res in all_scores.items()]
def extract_best_results(experiments, ktop=3, metric="accuracy", wordnet_metric=None, text_metric=None):
"""Extract best results"""
# Maps score names to column index in the numpy 2d array
# containing the results for a particular configuration
scores_map = {"accuracy":1, "f_measure":2, "precision":3, "recall":4}
best_results = np.zeros((ktop*2, 5))
corresponding_confs = np.array([(None, None, None, None, None,
None, None, None, None, None, None)]*(ktop*2), dtype=np.dtype('O'))
#print("best_results:", best_results.shape)
#print("corresponding_confs:", corresponding_confs.shape)
#print("Starts\n-------------------")
for conf, results in experiments.items():
# Skip text sim metrics distinct to <text_metric>
if text_metric and conf[10] != text_metric:
continue
# Skip wordnet sim metrics distinct to <wordnet_metric>
if wordnet_metric and conf[2] != wordnet_metric:
continue
# Sort by desired score in descending order and select best ktop
sorted_results = results[results[:,scores_map[metric]].argsort()[::-1]][:ktop, :]
#print("sorted results", sorted_results.shape)
# Configuration of results
results_conf = np.array([conf]*ktop, dtype=np.dtype('O'))
#print("results conf", sorted_results.shape)
corresponding_confs[ktop:,:] = results_conf
#print("corresponding_confs", corresponding_confs.shape)
#print(corresponding_confs)
# Vertically stacking best and new results
best_results[ktop:,:] = sorted_results[:ktop,:]
#print(best_results)
# Updating the best results if needed
idx = best_results[:,scores_map[metric]].argsort(axis=0)[::-1]
best_results = best_results[idx,:]
corresponding_confs = corresponding_confs[idx,:]
#print("New best results")
#print(best_results)
#print("New corresponding configurations")
#print(corresponding_confs)
# *** Declare best_results with twice the size of ktop and do everthing in-place ***
# Then compare running times
# Choose the new corresponding configurations to the new best results
#break
#print("\n")
return best_results[:ktop,:], corresponding_confs[:ktop,:]
bres, bconf = extract_best_results(experiments, ktop=3, metric="accuracy", wordnet_metric=None, text_metric=None)
print(bres)
print()
print(bconf) | [[0.7 0.74092247 0.81886792 0.77575561 0.86705412]
[0.7 0.74092247 0.81886792 0.77575561 0.86705412]
[0.7 0.74018646 0.81875749 0.77411003 0.86887032]]
[[False True 'lin' 'lemma' 'lemma' 'bnc_ic_2007' False 0.5 'all' 'binary'
'mihalcea']
[False True 'lin' 'lemma' 'lemma' 'bnc_ic_2007' False 0.5 'all' 'tf'
'mihalcea']
[False True 'lin' 'stem' 'token' 'bnc_ic_2007' False 0.5 'all' 'tf'
'mihalcea']]
| MIT | SoftCosine_Results_Analysis.ipynb | CubasMike/wordnet_paraphrase |
Getting some resultsIn this cell we get the best results for each text similarity metric and 3-top best wordnet metrics | my_results = []
for text_metric in ["mihalcea", "softcosine", "stevenson"]:
for wordnet_metric in ["path", "lch", "wup", "res", "jcn", "lin"]:
print("------------------------------------------------------")
print(text_metric, wordnet_metric)
res, conf = extract_best_results(experiments,
ktop=1,
metric="accuracy",
wordnet_metric=wordnet_metric,
text_metric=text_metric)
print(conf)
print(res)
print("\n")
my_results.append(np.concatenate((conf[0,:], res[0,0:3]), axis=-1))
res = np.array(sorted(my_results, key=lambda x:x[-2], reverse=True)) | _____no_output_____ | MIT | SoftCosine_Results_Analysis.ipynb | CubasMike/wordnet_paraphrase |
Saving results to latex table format 0. Removing stopwords flag : [True, False]1. Removing punctuation flag : [True, False]2. Wordnet similarity metrics : ["path", "lch", "wup", "res", "jcn", "lin"]3. Features extracted to compute similarity : ["token", "lemma", "stem", "lemmapos"]4. Features used to extract synsets : ["token", "lemma", "stem", "lemmapos"]5. Information Content used in some WordNet metrics : ["bnc_ic_2007", "bnc_ic_2000", "semcor_ic", "brown_ic"]6. Normalization flag : [True, False]7. Term-Term similarity minimum threslhold : [0.0, 0.25, 0.5, 0.75] 8. Synsets selection strategy (all-vs-all, first) : ["all", "first"]9. Features weighting scheme : ["tf", "binary"]10. Text similarity method : ["mihalcea", "softcosine", "stevenson"]11. Text similarity threshold : Real12. Accuracy : Real13. F1-score : Real | ic_map = {None: "", "bnc_ic_2007": "bnc07", "bnc_ic_2000": "bnc00", "semcor_ic":"semcor", "brown_ic":"brown"}
ss_map = {"first":"1", "all":"n"}
ws_map = {"tf":"tf", "binary":"bin"}
with open("latex_out.txt", "w") as fid:
for row in res:
line = "{0} & {1} & {3} & {9} & {4} & {8} & {2} & {5} & {6} & {7:.2f} & {10} & {11:.2f} & {12:.3f} & {13:.3f}".format(
r"\checkmark" if row[0] else "",
r"\checkmark" if row[1] else "",
row[2],
row[3],
row[4],
ic_map[row[5]],
r"\checkmark" if row[6] else "",
row[7],
ss_map[row[8]],
ws_map[row[9]],
row[10],
row[11],
row[12],
row[13]
)+"\\\\\n"
#line = " & ".join([str(x) for x in row]) +"\\\\\n"
fid.write(line)
test_configurations = []
for row in res:
test_configurations.append(tuple(row[:-3]))
with open("test_configurations.pickle", "wb") as fid:
pickle.dump(test_configurations, fid) | _____no_output_____ | MIT | SoftCosine_Results_Analysis.ipynb | CubasMike/wordnet_paraphrase |
Analyzing test scores | test_scores = pickle.load(open("./Results/test_results_20180504.pickle","rb"))
test_scores
ic_map = {None: "", "bnc_ic_2007": "bnc07", "bnc_ic_2000": "bnc00", "semcor_ic":"semcor", "brown_ic":"brown"}
ss_map = {"first":"1", "all":"n"}
ws_map = {"tf":"tf", "binary":"bin"}
new_scores = []
for old_row in res:
conf = tuple(old_row[:-3])
scores_mat = test_scores[conf]
scores = [x for x in scores_mat if x[0] == old_row[-3]][0]
new_scores.append((conf[-1], conf[2], scores[1], scores[2]))
new_scores.sort(key=lambda x:x[2], reverse=True)
with open("latex_test_out.txt", "w") as fid:
for row in new_scores:
line = "{0} & {1} & {2:.3f} & {3:.3f} \\\\\n".format(*row)
fid.write(line) | _____no_output_____ | MIT | SoftCosine_Results_Analysis.ipynb | CubasMike/wordnet_paraphrase |
Other example with chest information Training Prepare function to calculate feature vector from 3D to [1D x number_of_features] | def localization_fv2(data3d, voxelsize_mm): # scale
fv = []
# f0 = scipy.ndimage.filters.gaussian_filter(data3d, sigma=3).reshape(-1, 1)
#f1 = scipy.ndimage.filters.gaussian_filter(data3dr, sigma=1).reshape(-1, 1) - f0
#f2 = scipy.ndimage.filters.gaussian_filter(data3dr, sigma=5).reshape(-1, 1) - f0
#f3 = scipy.ndimage.filters.gaussian_filter(data3dr, sigma=10).reshape(-1, 1) - f0
#f4 = scipy.ndimage.filters.gaussian_filter(data3dr, sigma=20).reshape(-1, 1) - f0
# position asdfas
import bodynavigation as bn
ss = bn.BodyNavigation(data3d, voxelsize_mm)
fd1 = ss.dist_to_lungs().reshape(-1, 1)
fd2 = ss.dist_to_spine().reshape(-1, 1)
fd3 = ss.dist_sagittal().reshape(-1, 1)
fd4 = ss.dist_coronal().reshape(-1, 1)
fd5 = ss.dist_axial().reshape(-1, 1)
fd6 = ss.dist_to_surface().reshape(-1, 1)
fd7 = ss.dist_diaphragm().reshape(-1, 1)
fd8 = ss.dist_to_chest().reshape(-1, 1)
# f6 = scipy.ndimage.filters.gaussian_filter(data3d, sigma=[20, 1, 1]).reshape(-1, 1) - f0
# f7 = scipy.ndimage.filters.gaussian_filter(data3d, sigma=[1, 20, 1]).reshape(-1, 1) - f0
# f8 = scipy.ndimage.filters.gaussian_filter(data3d, sigma=[1, 1, 20]).reshape(-1, 1) - f0
# print "fv shapes ", f0.shape, fd2.shape, fd3.shape
fv = np.concatenate([
# f0,
# f1, f2, f3, f4,
fd1, fd2, fd3, fd4, fd5, fd6, fd7,
#f6, f7, f8
], 1)
return fv
import imtools.trainer3d
import imtools.datasets
ol = imtools.trainer3d.Trainer3D()
ol.feature_function = localization_fv2
for one in imtools.datasets.sliver_reader(
"*[0-2].mhd", read_seg=True,
sliver_reference_dir=sliver_reference_dir):
numeric_label, vs_mm, oname, orig_data, rname, ref_data = one
ol.add_train_data(orig_data, ref_data, voxelsize_mm=vs_mm)
ol.fit() | /Users/mjirik/miniconda/lib/python2.7/site-packages/numpy/core/numeric.py:190: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
a = empty(shape, dtype, order)
/Users/mjirik/miniconda/lib/python2.7/site-packages/skimage/morphology/misc.py:122: UserWarning: Only one label was provided to `remove_small_objects`. Did you mean to use a boolean array?
warn("Only one label was provided to `remove_small_objects`. "
/Users/mjirik/miniconda/lib/python2.7/site-packages/scipy/ndimage/interpolation.py:568: UserWarning: From scipy 0.13.0, the output shape of zoom() is calculated with round() instead of int() - for these inputs the size of the returned array has changed.
"the returned array has changed.", UserWarning)
| MIT | examples/liver_localization_training_with_chest.ipynb | mjirik/bodynavigation |
Testing | one = list(imtools.datasets.sliver_reader("*019.mhd", read_seg=True))[0]
numeric_label, vs_mm, oname, orig_data, rname, ref_data = one
fit = ol.predict(orig_data, voxelsize_mm=vs_mm)
plt.figure(figsize=(15,10))
sed3.show_slices(orig_data, fit, slice_step=20, axis=1, flipV=True)
import lisa.volumetry_evaluation
lisa.volumetry_evaluation.compare_volumes_sliver(ref_data, fit, vs_mm) | _____no_output_____ | MIT | examples/liver_localization_training_with_chest.ipynb | mjirik/bodynavigation |
Fitting the parameters | from scipy import optimize
from scipy import integrate
ydata = np.array(df_analyse.Germany[35:]) #90
time = np.arange(len(ydata))
I0 = ydata[0]
S0 = 1000000
R0 = 0
beta
print(I0)
def SIR_model_fit(SIR, time, beta, gamma):
S,I,R = SIR
dS = -beta * S * I/N0
dI = beta * S * I/N0 - gamma * I
dR = gamma * I
return([dS, dI, dR])
def fit_odeint(x,beta,gamma):
return integrate.odeint(SIR_model_fit, (S0,I0,R0), time, args=(beta, gamma))[:,1]
# [,:1] infected rate
# Integrate
popt = [0.4, 0.1] #beta, gamma
fit_odeint(time, *popt)
popt, pcov = optimize.curve_fit(fit_odeint, time, ydata)
perr = np.sqrt(np.diag(pcov))
print('Standard deviation errors : ', str(perr), 'Infection Start : ', ydata[0])
fitted = fit_odeint(time, *popt)
plt.semilogy(time, ydata, 'o')
plt.semilogy(time, fitted)
plt.title('SIR model for Germany')
plt.ylabel('Number of infected people')
plt.xlabel('Days')
plt.show()
print('Optimal Parameters : beta = ', popt[0], 'gamma = ', popt[1])
print('Reproduction number, R0 : ', popt[0]/popt[1]) | _____no_output_____ | MIT | notebooks/SIR_modeling/.ipynb_checkpoints/0_SIR_modeling_intro-checkpoint.ipynb | ebinzacharias/ads_COVID-19 |
Dynamic Beta | t_initial = 25
t_intro_measures = 14
t_hold = 21
t_relax = 21
beta_max = 0.4
beta_min = 0.11
gamma = 0.1
pd_beta = np.concatenate((np.array(t_initial*[beta_max]),
np.linspace(beta_max, beta_min, t_intro_measures),
np.array(t_hold * [beta_min]),
np.linspace(beta_min, beta_max, t_relax)
))
pd_beta
SIR = np.array([S0,I0,R0])
propagation_rates = pd.DataFrame(columns={'Susceptible':S0,
'Infected':I0,
'Recovered':R0
})
for each_beta in pd_beta:
new_delta_vector = SIR_model(SIR, each_beta, gamma)
SIR = SIR + new_delta_vector
propagation_rates = propagation_rates.append({'Susceptible':SIR[0],
'Infected':SIR[1],
'Recovered':SIR[2],
},ignore_index=True
)
fig, ax1 = plt.subplots(1,1)
ax1.plot(propagation_rates.index, propagation_rates.Infected, label = 'Infected', linewidth = 3)
#ax1.plot(propagation_rates.index, propagation_rates.Recovered, label = 'Recovered')
#ax1.plot(propagation_rates.index, propagation_rates.Susceptible, label = 'Susceptible')
ax1.bar(np.arange(len(ydata)), ydata, width=2, label = 'Actual cases in Germany', color = 'r')
t_phases = np.array([t_initial, t_intro_measures, t_hold, t_relax]).cumsum()
ax1.axvspan(0, t_phases[0], facecolor='b', alpha=0.2, label="No Measures")
ax1.axvspan(t_phases[0], t_phases[1], facecolor='b', alpha=0.3, label="Hard Measures")
ax1.axvspan(t_phases[1], t_phases[2], facecolor='b', alpha=0.4, label="Holding Measures")
ax1.axvspan(t_phases[2], t_phases[3], facecolor='b', alpha=0.5, label="Relaxed Measures")
ax1.axvspan(t_phases[3], len(propagation_rates.Infected),facecolor='b', alpha=0.6, label="Hard Measures Again")
ax1.set_ylim(10,1.5*max(propagation_rates.Infected))
#ax1.set_xlim(0,100)
ax1.set_yscale('log')
ax1.set_title('SIR Simulation', size= 16)
ax1.set_xlabel('Number of days', size=16)
ax1.legend(loc='best',
prop={'size':16}) | _____no_output_____ | MIT | notebooks/SIR_modeling/.ipynb_checkpoints/0_SIR_modeling_intro-checkpoint.ipynb | ebinzacharias/ads_COVID-19 |
Imports | import json
import re
import string
import scipy
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm_notebook as tqdm
from nltk.sentiment.util import mark_negation
from nltk import wordpunct_tokenize
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem.snowball import SnowballStemmer
from sklearn.linear_model import LinearRegression,SGDClassifier,ElasticNet,LogisticRegression
from sklearn.ensemble import GradientBoostingClassifier,VotingClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score,f1_score,mean_squared_error,confusion_matrix
from sklearn.feature_extraction.text import TfidfVectorizer,CountVectorizer | _____no_output_____ | MIT | A1_part_1/Non Pipelined Tester.ipynb | ankurshaswat/COL772 |
Constants | train_path = 'data/train.json'
dev_path = 'data/dev.json'
translator = str.maketrans("","", string.punctuation)
# stemmer = SnowballStemmer("english", ignore_stopwords=True) | _____no_output_____ | MIT | A1_part_1/Non Pipelined Tester.ipynb | ankurshaswat/COL772 |
Function Defs | def read_file(path):
data_X = []
data_Y = []
with open(path, 'r') as data_file:
line = data_file.readline()
while line:
data = json.loads(line)
data_X.append(data['review'])
data_Y.append(data['ratings'])
line = data_file.readline()
return data_X,data_Y
def get_metrics_from_pred(y_pred,y_true):
mse = mean_squared_error(y_pred,y_true)
try:
f1_scor = f1_score(y_true, y_pred, average='weighted')
acc = accuracy_score(y_true, y_pred)
conf_matrix = confusion_matrix(y_true,y_pred)
except:
y_pred = np.round(y_pred)
f1_scor = f1_score(y_true, y_pred, average='weighted')
acc = accuracy_score(y_true, y_pred)
conf_matrix = confusion_matrix(y_true,y_pred)
print("MSE = ",mse," F1 = ",f1_scor," Accuracy = ",acc)
plt.matshow(conf_matrix)
plt.colorbar()
def get_metrics(model,X,y_true):
y_pred = model.predict(X)
get_metrics_from_pred(y_pred,y_true)
def get_metrics_using_probs(model,X,y_true):
y_pred = model.predict_proba(X)
y_pred = np.average(y_pred,axis=1, weights=[1,2,3,4,5])*15
get_metrics_from_pred(y_pred,y_true)
def remove_repeats(sentence):
pattern = re.compile(r"(.)\1{2,}")
return pattern.sub(r"\1\1", sentence)
def tokenizer1(sentence):
sentence = sentence.translate(translator) # Remove punctuations
sentence = sentence.lower() # Convert to lowercase
sentence = re.sub(r'\d+', '', sentence) # Remove Numbers
sentence = remove_repeats(sentence) # Remove repeated characters
# sentence = sentence.strip() # Remove Whitespaces
tokens = wordpunct_tokenize(sentence) # Tokenize
# tokens = word_tokenize(sentence) # Tokenize
# for i in range(len(tokens)): # Stem word
# tokens[i] = stemmer.stem(tokens[i])
return tokens
# emoticon_string = r"""
# (?:
# [<>]?
# [:;=8] # eyes
# [\-o\*\']? # optional nose
# [\)\]\(\[dDpP/\:\}\{@\|\\] # mouth
# |
# [\)\]\(\[dDpP/\:\}\{@\|\\] # mouth
# [\-o\*\']? # optional nose
# [:;=8] # eyes
# [<>]?
# )"""
# # The components of the tokenizer:
# regex_strings = (
# # Phone numbers:
# r"""
# (?:
# (?: # (international)
# \+?[01]
# [\-\s.]*
# )?
# (?: # (area code)
# [\(]?
# \d{3}
# [\-\s.\)]*
# )?
# \d{3} # exchange
# [\-\s.]*
# \d{4} # base
# )"""
# ,
# # Emoticons:
# emoticon_string
# ,
# # HTML tags:
# r"""<[^>]+>"""
# ,
# # Twitter username:
# r"""(?:@[\w_]+)"""
# ,
# # Twitter hashtags:
# r"""(?:\#+[\w_]+[\w\'_\-]*[\w_]+)"""
# ,
# # Remaining word types:
# r"""
# (?:[a-z][a-z'\-_]+[a-z]) # Words with apostrophes or dashes.
# |
# (?:[+\-]?\d+[,/.:-]\d+[+\-]?) # Numbers, including fractions, decimals.
# |
# (?:[\w_]+) # Words without apostrophes or dashes.
# |
# (?:\.(?:\s*\.){1,}) # Ellipsis dots.
# |
# (?:\S) # Everything else that isn't whitespace.
# """
# )
# ######################################################################
# # This is the core tokenizing regex:
# word_re = re.compile(r"""(%s)""" % "|".join(regex_strings), re.VERBOSE | re.I | re.UNICODE)
# # The emoticon string gets its own regex so that we can preserve case for them as needed:
# emoticon_re = re.compile(regex_strings[1], re.VERBOSE | re.I | re.UNICODE)
# # These are for regularizing HTML entities to Unicode:
# html_entity_digit_re = re.compile(r"&#\d+;")
# html_entity_alpha_re = re.compile(r"&\w+;")
# amp = "&"
# negation_re = re.compile(r"""
# never|no|nothing|nowhere|noone|none|not|
# havent|hasnt|hadnt|cant|couldnt|shouldnt|
# wont|wouldnt|dont|doesnt|didnt|isnt|arent|aint|
# n't|
# haven't|hasn't|hadn't|can't|couldn't|shouldn't|
# won't|wouldn't|don't|doesn't|didn't|isn't|aren't|ain't
# """,re.VERBOSE )
# clause_level_re = re.compile(r"""^[.:;!?]$""",re.VERBOSE )
# ######################################################################
# class Tokenizer:
# def __init__(self, preserve_case=False):
# self.preserve_case = preserve_case
# def tokenize(self, s):
# """
# Argument: s -- any string or unicode object
# Value: a tokenize list of strings; conatenating this list returns the original string if preserve_case=False
# """
# # Try to ensure unicode:
# # try:
# # s = unicode(s)
# # except UnicodeDecodeError:
# # s = str(s).encode('string_escape')
# # s = unicode(s)
# # Fix HTML character entitites:
# # Tokenize:
# words = word_re.findall(s)
# # Possible alter the case, but avoid changing emoticons like :D into :d:
# if not self.preserve_case:
# words = list(map((lambda x : x if emoticon_re.search(x) else x.lower()), words))
# # negator = False
# # for i in range(len(words)):
# # word = words[i]
# # if(negation_re.match(word)):
# # negator = !negator
# # elif(clause_level_re.match(word)):
# # negator = False
# # elif(negator):
# # words[i] = word+"_NEG"
# return words
# tok = Tokenizer().tokenize
tokenize = tokenizer1
# tokenize = tok
# for i in tqdm(range(len(X_train))):
# tokenize(X_train[i])
# for i in range(200,600):
# print(tokenize(X_train[i]))
X_train,Y_train = read_file(train_path)
X_dev,Y_dev = read_file(dev_path)
# processed_stopwords = []
# for word in stopwords.words('english'):
# processed_stopwords += tokenize(word)
# # print(processed_stopwords)
# vectorizer = TfidfVectorizer(strip_accents='ascii',
# lowercase=True,
# tokenizer=tokenize,
# stop_words=processed_stopwords,
# ngram_range=(1,1),
# binary=True,
# norm='l2',
# analyzer='word')
# vectorizer = TfidfVectorizer(binary=True,tokenizer=tokenize)
# vectorizer = TfidfVectorizer(tokenizer=tokenize)
vectorizer = TfidfVectorizer(tokenizer=tokenize,ngram_range=(1,2))
# vectorizer = CountVectorizer(tokenizer=tokenize,ngram_range=(1,2))
X_train_counts = vectorizer.fit_transform(X_train)
X_dev_counts = vectorizer.transform(X_dev)
# print(X_train_counts)
# from sklearn import preprocessing
# scaler = preprocessing.StandardScaler(with_mean=False).fit(X_train_counts)
# X_train_counts = scaler.transform(X_train_counts)
# X_dev_counts = scaler.transform(X_dev_counts)
# print(X_train_counts) | _____no_output_____ | MIT | A1_part_1/Non Pipelined Tester.ipynb | ankurshaswat/COL772 |
* Try Removeding whole numbers* Try seperating number and text* Try replacing 000ps by ooops* Try removing repeated characters like sssslllleeeepppp. Baseline | # all_5 = list(5*np.ones([len(Y_dev),]))
# get_metrics_from_pred(all_5,Y_dev) | _____no_output_____ | MIT | A1_part_1/Non Pipelined Tester.ipynb | ankurshaswat/COL772 |
Trying Multinomial Naive Bayes | # model = MultinomialNB()
# model.fit(X_train_counts,Y_train)
# get_metrics(model,X_dev_counts,Y_dev)
# get_metrics_using_probs(model,X_dev_counts,Y_dev) | _____no_output_____ | MIT | A1_part_1/Non Pipelined Tester.ipynb | ankurshaswat/COL772 |
Trying Logistic Regression | model = LogisticRegression(verbose=1,n_jobs=7,solver='sag',multi_class='ovr')
model.fit(X_train_counts,Y_train)
get_metrics(model,X_dev_counts,Y_dev)
get_metrics_using_probs(model,X_dev_counts,Y_dev)
# model = LogisticRegression(verbose=1,n_jobs=7,class_weight='balanced',multi_class='ovr',solver='liblinear')
# model.fit(X_train_counts,Y_train)
# get_metrics(model,X_dev_counts,Y_dev)
# get_metrics_using_probs(model,X_dev_counts,Y_dev)
# model = LogisticRegression(verbose=1,n_jobs=7,class_weight='balanced',multi_class='multinomial',solver='lbfgs')
# model.fit(X_train_counts,Y_train)
# get_metrics(model,X_dev_counts,Y_dev)
# get_metrics_using_probs(model,X_dev_counts,Y_dev)
# model = LogisticRegression(verbose=1,n_jobs=7,class_weight='balanced',multi_class='ovr',solver='liblinear',penalty='l1')
# model.fit(X_train_counts,Y_train)
# get_metrics(model,X_dev_counts,Y_dev)
# get_metrics_using_probs(model,X_dev_counts,Y_dev)
# model = LogisticRegression(verbose=1,n_jobs=7,class_weight='balanced',multi_class='multinomial',solver='saga',penalty='l1')
# model.fit(X_train_counts,Y_train)
# get_metrics(model,X_dev_counts,Y_dev)
# get_metrics_using_probs(model,X_dev_counts,Y_dev) | _____no_output_____ | MIT | A1_part_1/Non Pipelined Tester.ipynb | ankurshaswat/COL772 |
Linear Regression | # model = LinearRegression(n_jobs=7)
# model.fit(X_train_counts,Y_train)
# get_metrics(model,X_dev_counts,Y_dev) | _____no_output_____ | MIT | A1_part_1/Non Pipelined Tester.ipynb | ankurshaswat/COL772 |
SGD Classifier | # model = SGDClassifier(n_jobs=7,verbose=True)
# model.fit(X_train_counts,Y_train)
# get_metrics(model,X_dev_counts,Y_dev) | _____no_output_____ | MIT | A1_part_1/Non Pipelined Tester.ipynb | ankurshaswat/COL772 |
ElasticNet | # model = ElasticNet()
# model.fit(X_train_counts,Y_train)
# get_metrics(model,X_dev_counts,Y_dev) | _____no_output_____ | MIT | A1_part_1/Non Pipelined Tester.ipynb | ankurshaswat/COL772 |
GradientBoostingClassifier | # model = GradientBoostingClassifier(verbose=True)
# model.fit(X_train_counts,Y_train)
# get_metrics(model,X_dev_counts,Y_dev) | _____no_output_____ | MIT | A1_part_1/Non Pipelined Tester.ipynb | ankurshaswat/COL772 |
Complicated Model ( Tree with two branches 1-3 and 4-5) | indices = np.where(list(map(lambda x:x>3,Y_train)))[0]
X_train_counts_4_5 = X_train_counts[indices]
Y_train_4_5 = [Y_train[j] for j in indices]
indices = np.where(list(map(lambda x:x<=3,Y_train)))[0]
X_train_counts_1_3 = X_train_counts[indices]
Y_train_1_3 = [Y_train[j] for j in indices]
Y_modified = list(map(lambda x:int(x>3),Y_train))
model1 = LogisticRegression(verbose=1,n_jobs=7,solver='sag')
model1.fit(X_train_counts,Y_modified)
model2 = LogisticRegression(verbose=1,n_jobs=7,solver='sag')
model2.fit(X_train_counts_4_5,Y_train_4_5)
model3 = LogisticRegression(verbose=1,n_jobs=7,solver='sag',multi_class='ovr')
model3.fit(X_train_counts_1_3,Y_train_1_3)
pred1 = model1.predict(X_dev_counts)
pred2 = model2.predict_proba(X_dev_counts)
pred3 = model3.predict_proba(X_dev_counts)
pred = []
for i in tqdm(range(len(pred1))):
if(pred1[i] == 1):
pred.append(pred2[i][0]*4.0 + pred2[i][1]*5.0)
else:
pred.append(pred3[i][0]*1.0 + pred3[i][1]*2.0 + pred3[i][2]*3.0)
get_metrics_from_pred(pred,Y_dev) | _____no_output_____ | MIT | A1_part_1/Non Pipelined Tester.ipynb | ankurshaswat/COL772 |
Another Try (Tree with negative ,neutral and positive review) | indices = np.where(list(map(lambda x:x>3,Y_train)))[0]
X_train_counts_4_5 = X_train_counts[indices]
Y_train_4_5 = [Y_train[j] for j in indices]
indices = np.where(list(map(lambda x:x<3,Y_train)))[0]
X_train_counts_1_2 = X_train_counts[indices]
Y_train_1_2 = [Y_train[j] for j in indices]
indices = np.where(list(map(lambda x:x==3,Y_train)))[0]
X_train_counts_3 = X_train_counts[indices]
Y_train_3 = [Y_train[j] for j in indices]
def modif(x):
if (x==3):
return 1
elif(x>3):
return 2
else:
return 0
Y_modified = list(map(lambda x: modif(x),Y_train))
model1 = LogisticRegression(verbose=1,n_jobs=7,solver='sag',multi_class='ovr')
model1.fit(X_train_counts,Y_modified)
model2 = LogisticRegression(verbose=1,n_jobs=7,solver='sag')
model2.fit(X_train_counts_4_5,Y_train_4_5)
model3 = LogisticRegression(verbose=1,n_jobs=7,solver='sag')
model3.fit(X_train_counts_1_2,Y_train_1_2)
pred1 = model1.predict(X_dev_counts)
pred1_p = model1.predict_proba(X_dev_counts)
pred2 = model2.predict_proba(X_dev_counts)
pred3 = model3.predict_proba(X_dev_counts)
pred = []
for i in tqdm(range(len(pred1))):
if(pred1[i] == 0):
pred.append(pred3[i][0]*1.0 + pred3[i][1]*2.0)
elif(pred1[i] == 1):
pred.append(pred1_p[i][0]*1.5 + pred1_p[i][1]*3 + pred1_p[i][2]*4.5)
elif(pred1[i] == 2):
pred.append(pred2[i][0]*4.0 + pred2[i][1]*5.0)
get_metrics_from_pred(pred,Y_dev)
pred_n_3_p = model1.predict_proba(X_dev_counts)
pred_4_5 = model2.predict_proba(X_dev_counts)
pred_1_2 = model3.predict_proba(X_dev_counts)
pred = []
for i in tqdm(range(len(pred1))):
pred.append(pred_n_3_p[i][0]*pred_1_2[i][0]*1.0 + pred_n_3_p[i][0]*pred_1_2[i][1]*2.0 + pred_n_3_p[i][1]*3.0 + pred_n_3_p[i][2]*pred_4_5[i][0]*4.0 + pred_n_3_p[i][2]*pred_4_5[i][1]*5.0)
get_metrics_from_pred(pred,Y_dev) | _____no_output_____ | MIT | A1_part_1/Non Pipelined Tester.ipynb | ankurshaswat/COL772 |
Voting Classifier (With simple ovr logistive regression and multinomial naive bayes) | # m1 = LogisticRegression(verbose=1,n_jobs=7,solver='sag',multi_class='ovr')
# m2 = MultinomialNB()
# model = VotingClassifier(estimators=[('lr', m1),('gnb', m2)],voting='soft')
# model.fit(X_train_counts,Y_train)
# get_metrics(model,X_dev_counts,Y_dev)
# get_metrics_using_probs(model,X_dev_counts,Y_dev) | _____no_output_____ | MIT | A1_part_1/Non Pipelined Tester.ipynb | ankurshaswat/COL772 |
Binary Logistics Everywhere (Tree with base classified as neutral or 1-3&4-5) | indices = np.where(list(map(lambda x: x!=3,Y_train)))[0]
X_train_counts_p_n = X_train_counts[indices]
Y_train_p_n = [1 if Y_train[j]>3 else 0 for j in indices]
indices = np.where(list(map(lambda x:x>3,Y_train)))[0]
X_train_counts_4_5 = X_train_counts[indices]
Y_train_4_5 = [Y_train[j] for j in indices]
indices = np.where(list(map(lambda x:x<3,Y_train)))[0]
X_train_counts_1_2 = X_train_counts[indices]
Y_train_1_2 = [Y_train[j] for j in indices]
def modif(x):
if (x==3):
return 1
else:
return 0
Y_modified = list(map(lambda x: modif(x),Y_train))
model_neutral = LogisticRegression(verbose=1,n_jobs=7,solver='sag')
model_neutral.fit(X_train_counts,Y_modified)
model_n_p = LogisticRegression(verbose=1,n_jobs=7,solver='sag')
model_n_p.fit(X_train_counts_p_n,Y_train_p_n)
model_4_5 = LogisticRegression(verbose=1,n_jobs=7,solver='sag')
model_4_5.fit(X_train_counts_4_5,Y_train_4_5)
model_1_2 = LogisticRegression(verbose=1,n_jobs=7,solver='sag')
model_1_2.fit(X_train_counts_1_2,Y_train_1_2)
pred_neutral = model_neutral.predict_proba(X_dev_counts)
pred_n_p = model_n_p.predict_proba(X_dev_counts)
pred_1_2 = model_1_2.predict_proba(X_dev_counts)
pred_4_5 = model_4_5.predict_proba(X_dev_counts)
pred = []
for i in tqdm(range(len(pred_neutral))):
pred.append(pred_neutral[i][1]*3.0 +
pred_neutral[i][0]*pred_n_p[i][0]*pred_1_2[i][0]*1.0 +
pred_neutral[i][0]*pred_n_p[i][0]*pred_1_2[i][1]*2.0+
pred_neutral[i][0]*pred_n_p[i][1]*pred_4_5[i][0]*4.0+
pred_neutral[i][0]*pred_n_p[i][1]*pred_4_5[i][1]*5.0)
get_metrics_from_pred(pred,Y_dev)
pred_neutral_c = model_neutral.predict(X_dev_counts)
pred_neutral = model_neutral.predict_proba(X_dev_counts)
pred_n_p_c = model_n_p.predict(X_dev_counts)
pred_n_p = model_n_p.predict_proba(X_dev_counts)
pred_1_2_c = model_1_2.predict(X_dev_counts)
pred_1_2 = model_1_2.predict_proba(X_dev_counts)
pred_4_5_c = model_4_5.predict(X_dev_counts)
pred_4_5 = model_4_5.predict_proba(X_dev_counts)
pred = []
for i in tqdm(range(len(pred_neutral))):
if(pred_neutral_c[i] == 1):
pred.append(3)
else:
if(pred_n_p_c[i] == 0):
pred.append(pred_1_2_c[i])
else:
pred.append(pred_4_5_c[i])
get_metrics_from_pred(pred,Y_dev) | _____no_output_____ | MIT | A1_part_1/Non Pipelined Tester.ipynb | ankurshaswat/COL772 |
Another Try (Full tree leaning towards 1) | indices = np.where(list(map(lambda x: x<=3,Y_train)))[0]
X_train_counts_12_3 = X_train_counts[indices]
Y_train_12_3 = [1 if Y_train[j]==3 else 0 for j in indices]
indices = np.where(list(map(lambda x:x>3,Y_train)))[0]
X_train_counts_4_5 = X_train_counts[indices]
Y_train_4_5 = [Y_train[j] for j in indices]
indices = np.where(list(map(lambda x:x<3,Y_train)))[0]
X_train_counts_1_2 = X_train_counts[indices]
Y_train_1_2 = [Y_train[j] for j in indices]
def modif(x):
if (x>3):
return 1
else:
return 0
Y_modified = list(map(lambda x: modif(x),Y_train))
model_123_45 = LogisticRegression(verbose=1,n_jobs=7,solver='sag')
model_123_45.fit(X_train_counts,Y_modified)
model_4_5 = LogisticRegression(verbose=1,n_jobs=7,solver='sag')
model_4_5.fit(X_train_counts_4_5,Y_train_4_5)
model_12_3 = LogisticRegression(verbose=1,n_jobs=7,solver='sag')
model_12_3.fit(X_train_counts_12_3,Y_train_12_3)
model_1_2 = LogisticRegression(verbose=1,n_jobs=7,solver='sag')
model_1_2.fit(X_train_counts_1_2,Y_train_1_2)
pred_123_45 = model_123_45.predict_proba(X_dev_counts)
pred_12_3 = model_12_3.predict_proba(X_dev_counts)
pred_1_2 = model_1_2.predict_proba(X_dev_counts)
pred_4_5 = model_4_5.predict_proba(X_dev_counts)
pred = []
for i in tqdm(range(len(pred_neutral))):
pred.append(pred_123_45[i][0]*pred_12_3[i][0]*pred_1_2[i][0]*1.0+
pred_123_45[i][0]*pred_12_3[i][0]*pred_1_2[i][1]*2.0+
pred_123_45[i][0]*pred_12_3[i][1]*3.0+
pred_123_45[i][1]*pred_4_5[i][0]*4.0+
pred_123_45[i][1]*pred_4_5[i][1]*5.0)
get_metrics_from_pred(pred,Y_dev) | _____no_output_____ | MIT | A1_part_1/Non Pipelined Tester.ipynb | ankurshaswat/COL772 |
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